refactor: adjust resultant force zero threshold from 0.4 to 0.1

devkit/build/je-skin-devkit-server/EXE-00.toc

@@ -1,363 +0,0 @@
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-  ('numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\philox\\LICENSE.md',
-   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\philox\\LICENSE.md',
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-  ('numpy-2.4.4.dist-info\\licenses\\numpy\\fft\\pocketfft\\LICENSE.md',
-   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\fft\\pocketfft\\LICENSE.md',
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-  ('numpy-2.4.4.dist-info\\licenses\\numpy\\linalg\\lapack_lite\\LICENSE.txt',
-   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\linalg\\lapack_lite\\LICENSE.txt',
-   'DATA'),
-  ('numpy-2.4.4.dist-info\\RECORD',
-   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\RECORD',
-   'DATA'),
-  ('base_library.zip',
-   'D:\\JE-Skin\\devkit\\build\\je-skin-devkit-server\\base_library.zip',
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devkit/build/je-skin-devkit-server/warn-je-skin-devkit-server.txt

@@ -1,245 +0,0 @@
-
-This file lists modules PyInstaller was not able to find. This does not
-necessarily mean these modules are required for running your program. Both
-Python's standard library and 3rd-party Python packages often conditionally
-import optional modules, some of which may be available only on certain
-platforms.
-
-Types of import:
-* top-level: imported at the top-level - look at these first
-* conditional: imported within an if-statement
-* delayed: imported within a function
-* optional: imported within a try-except-statement
-
-IMPORTANT: Do NOT post this list to the issue-tracker. Use it as a basis for
-            tracking down the missing module yourself. Thanks!
-
-missing module named pwd - imported by posixpath (delayed, conditional, optional), subprocess (delayed, conditional, optional), shutil (delayed, optional), tarfile (optional), pathlib (optional), netrc (delayed, optional), http.server (delayed, optional)
-missing module named grp - imported by subprocess (delayed, conditional, optional), shutil (delayed, optional), tarfile (optional), pathlib (optional)
-missing module named 'collections.abc' - imported by typing (top-level), tracemalloc (top-level), traceback (top-level), _colorize (top-level), selectors (top-level), logging (top-level), http.client (top-level), importlib.resources.readers (top-level), inspect (top-level), multiprocessing.managers (top-level), typing_extensions (top-level), asyncio.base_events (top-level), asyncio.coroutines (top-level), grpc.aio._metadata (top-level), google.protobuf.internal.containers (top-level), google.protobuf.internal.well_known_types (top-level), numpy._typing._array_like (top-level), numpy._typing._nested_sequence (conditional), numpy._typing._shape (top-level), numpy._typing._dtype_like (top-level), numpy.lib._function_base_impl (top-level), _pyrepl.types (top-level), numpy.lib._npyio_impl (top-level), numpy.random._common (top-level), numpy.random._generator (top-level), numpy.random.bit_generator (top-level), numpy.random.mtrand (top-level), numpy.polynomial._polybase (top-level), xml.etree.ElementTree (top-level)
-missing module named _posixsubprocess - imported by subprocess (conditional), multiprocessing.util (delayed)
-missing module named fcntl - imported by subprocess (optional), pathlib._os (optional)
-missing module named _posixshmem - imported by multiprocessing.resource_tracker (conditional), multiprocessing.shared_memory (conditional)
-missing module named _scproxy - imported by urllib.request (conditional)
-missing module named posix - imported by os (conditional, optional), posixpath (optional), shutil (conditional), importlib._bootstrap_external (conditional), pathlib._os (optional), _pyrepl.trace (conditional)
-missing module named resource - imported by posix (top-level)
-missing module named _frozen_importlib_external - imported by importlib._bootstrap (delayed), importlib (optional), importlib.abc (optional), zipimport (top-level)
-excluded module named _frozen_importlib - imported by importlib (optional), importlib.abc (optional), zipimport (top-level)
-missing module named multiprocessing.BufferTooShort - imported by multiprocessing (top-level), multiprocessing.connection (top-level)
-missing module named multiprocessing.AuthenticationError - imported by multiprocessing (top-level), multiprocessing.forkserver (top-level), multiprocessing.connection (top-level)
-missing module named multiprocessing.get_context - imported by multiprocessing (top-level), multiprocessing.pool (top-level), multiprocessing.managers (top-level), multiprocessing.sharedctypes (top-level)
-missing module named multiprocessing.TimeoutError - imported by multiprocessing (top-level), multiprocessing.pool (top-level)
-missing module named multiprocessing.set_start_method - imported by multiprocessing (top-level), multiprocessing.spawn (top-level)
-missing module named multiprocessing.get_start_method - imported by multiprocessing (top-level), multiprocessing.spawn (top-level)
-missing module named pyimod02_importers - imported by C:\Python314\Lib\site-packages\PyInstaller\hooks\rthooks\pyi_rth_pkgutil.py (delayed)
-missing module named _dummy_thread - imported by numpy._core.arrayprint (optional)
-missing module named 'numpy_distutils.cpuinfo' - imported by numpy.f2py.diagnose (delayed, conditional, optional)
-missing module named 'numpy_distutils.fcompiler' - imported by numpy.f2py.diagnose (delayed, conditional, optional)
-missing module named 'numpy_distutils.command' - imported by numpy.f2py.diagnose (delayed, conditional, optional)
-missing module named numpy_distutils - imported by numpy.f2py.diagnose (delayed, optional)
-missing module named charset_normalizer - imported by numpy.f2py.crackfortran (optional)
-missing module named vms_lib - imported by platform (delayed, optional)
-missing module named 'java.lang' - imported by platform (delayed, optional)
-missing module named java - imported by platform (delayed)
-missing module named psutil - imported by numpy.testing._private.utils (delayed, optional)
-missing module named termios - imported by tty (top-level), _pyrepl.pager (delayed, optional)
-missing module named readline - imported by cmd (delayed, conditional, optional), code (delayed, conditional, optional), pdb (delayed, conditional, optional), rlcompleter (optional)
-missing module named win32pdh - imported by numpy.testing._private.utils (delayed, conditional)
-missing module named _typeshed - imported by numpy.random.bit_generator (top-level)
-missing module named numpy.random.RandomState - imported by numpy.random (top-level), numpy.random._generator (top-level)
-missing module named threadpoolctl - imported by numpy.lib._utils_impl (delayed, optional)
-missing module named numpy._core.zeros - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.vstack - imported by numpy._core (top-level), numpy.lib._shape_base_impl (top-level), numpy (conditional)
-missing module named numpy._core.void - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.vecmat - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.vecdot - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.ushort - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.unsignedinteger - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.ulonglong - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.ulong - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.uintp - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.uintc - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.uint64 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.uint32 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.uint16 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.uint - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.ubyte - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.trunc - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.true_divide - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.transpose - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._function_base_impl (top-level), numpy (conditional)
-missing module named numpy._core.trace - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.timedelta64 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.tensordot - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.tanh - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.tan - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.swapaxes - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.sum - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.subtract - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.str_ - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.square - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.sqrt - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional), numpy.fft._pocketfft (top-level)
-missing module named numpy._core.spacing - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.sort - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.sinh - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.single - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.signedinteger - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.signbit - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.sign - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.short - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.rint - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.right_shift - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.result_type - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional), numpy.fft._pocketfft (top-level)
-missing module named numpy._core.remainder - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.reciprocal - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional), numpy.fft._pocketfft (top-level)
-missing module named numpy._core.radians - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.rad2deg - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.prod - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.power - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.positive - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.pi - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.outer - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.ones - imported by numpy._core (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
-missing module named numpy._core.object_ - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.number - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.not_equal - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.nextafter - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.newaxis - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.negative - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.ndarray - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy.lib._utils_impl (top-level), numpy (conditional)
-missing module named numpy._core.multiply - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.moveaxis - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.modf - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.mod - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.minimum - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.maximum - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.max - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.matvec - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.matrix_transpose - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.matmul - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.longlong - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.longdouble - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.long - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.logical_xor - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.logical_or - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.logical_not - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.logical_and - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.logaddexp2 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.logaddexp - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.log10 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.log2 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.log1p - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.log - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.linspace - imported by numpy._core (top-level), numpy.lib._index_tricks_impl (top-level), numpy (conditional)
-missing module named numpy._core.less_equal - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.less - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.left_shift - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.ldexp - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.lcm - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.isscalar - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy.lib._polynomial_impl (top-level), numpy (conditional)
-missing module named numpy._core.isnat - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy (conditional)
-missing module named numpy._core.isnan - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.isfinite - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.intp - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (top-level), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.integer - imported by numpy._core (conditional), numpy (conditional), numpy.fft._helper (top-level)
-missing module named numpy._core.intc - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.int64 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.int32 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.int16 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.int8 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.inf - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.inexact - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.iinfo - imported by numpy._core (top-level), numpy.lib._twodim_base_impl (top-level), numpy (conditional)
-missing module named numpy._core.hypot - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.hstack - imported by numpy._core (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
-missing module named numpy._core.heaviside - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.half - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.greater_equal - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.greater - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.gcd - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.frompyfunc - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.frexp - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.fmod - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.fmin - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.fmax - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.floor_divide - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.floor - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.floating - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.float_power - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.float32 - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.float16 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.finfo - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
-missing module named numpy._core.fabs - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.expm1 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.exp2 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.exp - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.euler_gamma - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.errstate - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.equal - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.empty_like - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional), numpy.fft._pocketfft (top-level)
-missing module named numpy._core.empty - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (top-level), numpy (conditional), numpy.fft._helper (top-level)
-missing module named numpy._core.e - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.double - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.dot - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
-missing module named numpy._core.divmod - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.divide - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.diagonal - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.degrees - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.deg2rad - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.datetime64 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.csingle - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.cross - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.count_nonzero - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.cosh - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.cos - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.copysign - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.conjugate - imported by numpy._core (conditional), numpy (conditional), numpy.fft._pocketfft (top-level)
-missing module named numpy._core.conj - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.complexfloating - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.complex64 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
-missing module named numpy._core.clongdouble - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.character - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.ceil - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.cdouble - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.cbrt - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.bytes_ - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.byte - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.bool_ - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.bitwise_xor - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.bitwise_or - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.bitwise_count - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.bitwise_and - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.atleast_3d - imported by numpy._core (top-level), numpy.lib._shape_base_impl (top-level), numpy (conditional)
-missing module named numpy._core.atleast_2d - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.atleast_1d - imported by numpy._core (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
-missing module named numpy._core.asarray - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._array_utils_impl (top-level), numpy (conditional), numpy.fft._helper (top-level), numpy.fft._pocketfft (top-level)
-missing module named numpy._core.asanyarray - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.array_repr - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy (conditional)
-missing module named numpy._core.array2string - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.array - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
-missing module named numpy._core.argsort - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.arctanh - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.arctan2 - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.arctan - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.arcsinh - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.arcsin - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.arccosh - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.arccos - imported by numpy._core (conditional), numpy (conditional)
-missing module named numpy._core.arange - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy (conditional), numpy.fft._helper (top-level)
-missing module named numpy._core.amin - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.amax - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named numpy._core.all - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
-missing module named numpy._core.add - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
-missing module named yaml - imported by numpy.__config__ (delayed)
-missing module named numpy._distributor_init_local - imported by numpy (optional), numpy._distributor_init (optional)
-missing module named defusedxml - imported by openpyxl.xml (delayed, optional)
-missing module named lxml - imported by openpyxl.xml (delayed, optional)
-missing module named 'defusedxml.ElementTree' - imported by openpyxl.xml.functions (conditional)
-missing module named 'lxml.etree' - imported by openpyxl.xml.functions (conditional)
-missing module named PIL - imported by openpyxl.drawing.image (optional)
-missing module named openpyxl.tests - imported by openpyxl.reader.excel (optional)
-missing module named google.protobuf.pyext._message - imported by google.protobuf.pyext (conditional, optional), google.protobuf.internal.api_implementation (conditional, optional), google.protobuf.descriptor (conditional), google.protobuf.pyext.cpp_message (conditional)
-missing module named google.protobuf.enable_deterministic_proto_serialization - imported by google.protobuf (optional), google.protobuf.internal.api_implementation (optional)
-missing module named google.protobuf.internal._api_implementation - imported by google.protobuf.internal (optional), google.protobuf.internal.api_implementation (optional)
-missing module named grpc_reflection - imported by grpc (optional)
-missing module named grpc_health - imported by grpc (optional)
-missing module named pkg_resources - imported by grpc_tools.protoc (conditional)

package-lock.json

@@ -6,7 +6,7 @@
   "packages": {
     "": {
       "name": "JE-Skin",
-      "version": "0.3.0",
+      "version": "0.4.0",
       "license": "MIT",
       "dependencies": {
         "@tauri-apps/api": "^2",

src-tauri/.cargo/config.toml

@@ -1,2 +0,0 @@
-[registries.kellnr]
-index = "sparse+http://crates.huangyanjie.com/api/v1/crates/"

src-tauri/Cargo.lock

@@ -14,7 +14,6 @@ dependencies = [
  "crc",
  "csv",
  "dirs",
- "eskin-finger-sdk",
  "fern",
  "futures-util",
  "humantime",
@@ -1153,25 +1152,6 @@ dependencies = [
  "windows-sys 0.61.2",
 ]
 
-[[package]]
-name = "eskin-finger-sdk"
-version = "0.1.0"
-source = "sparse+http://crates.huangyanjie.com/api/v1/crates/"
-checksum = "341d54dbc70a0fb7cdd04162cdda6ab5735f9a4f717b1921b42c00e8afc37bb9"
-dependencies = [
- "chrono",
- "crc",
- "crossbeam-channel",
- "fern",
- "libc",
- "log",
- "serde",
- "serde_json",
- "serialport",
- "thiserror 2.0.18",
- "uuid",
-]
-
 [[package]]
 name = "event-listener"
 version = "5.4.1"
@@ -2334,9 +2314,9 @@ dependencies = [
 
 [[package]]
 name = "libc"
-version = "0.2.186"
+version = "0.2.183"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "68ab91017fe16c622486840e4c83c9a37afeff978bd239b5293d61ece587de66"
+checksum = "b5b646652bf6661599e1da8901b3b9522896f01e736bad5f723fe7a3a27f899d"
 
 [[package]]
 name = "libloading"
@@ -2360,26 +2340,6 @@ dependencies = [
  "redox_syscall 0.7.4",
 ]
 
-[[package]]
-name = "libudev"
-version = "0.3.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "78b324152da65df7bb95acfcaab55e3097ceaab02fb19b228a9eb74d55f135e0"
-dependencies = [
- "libc",
- "libudev-sys",
-]
-
-[[package]]
-name = "libudev-sys"
-version = "0.1.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3c8469b4a23b962c1396b9b451dda50ef5b283e8dd309d69033475fa9b334324"
-dependencies = [
- "libc",
- "pkg-config",
-]
-
 [[package]]
 name = "linux-raw-sys"
 version = "0.12.1"
@@ -4303,7 +4263,6 @@ dependencies = [
  "core-foundation",
  "core-foundation-sys",
  "io-kit-sys",
- "libudev",
  "mach2",
  "nix 0.26.4",
  "scopeguard",
@@ -5606,9 +5565,9 @@ checksum = "b6c140620e7ffbb22c2dee59cafe6084a59b5ffc27a8859a5f0d494b5d52b6be"
 
 [[package]]
 name = "uuid"
-version = "1.23.1"
+version = "1.22.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ddd74a9687298c6858e9b88ec8935ec45d22e8fd5e6394fa1bd4e99a87789c76"
+checksum = "a68d3c8f01c0cfa54a75291d83601161799e4a89a39e0929f4b0354d88757a37"
 dependencies = [
  "getrandom 0.4.2",
  "js-sys",

src-tauri/Cargo.toml

@@ -49,11 +49,10 @@ crc = "3.4.0"
 axum = { version = "0.8", features = ["ws"] }
 tower-http = { version = "0.6", features = ["cors"] }
 futures-util = "0.3"
-uuid = { version = "1.23", features = ["v4", "serde"] }
+uuid = { version = "1", features = ["v4", "serde"] }
 rand = "0.8"
 reqwest = { version = "0.12", default-features = false, features = ["json", "rustls-tls"] }
 ndarray = { version = "0.15", optional = true }
-eskin-finger-sdk = { version = "0.1.0", registry = "kellnr" }
 
 [target.'cfg(not(any(target_os = "android", target_os = "ios")))'.dependencies]
 tauri-plugin-updater = "2"

src-tauri/resources/model_params.json

@@ -1,217 +0,0 @@
-{
-  "scaler_mean": [
-    1748.7541486595198,
-    1292.5704664084863,
-    669.8700117864961,
-    1617.8798712839798,
-    2104.589811228976,
-    3267.658809002638,
-    3366.4000112252343,
-    2660.981740285495,
-    2656.615909898786,
-    1747.1196048717518,
-    3093.4178032216423,
-    3107.599371386878,
-    4138.929019101607,
-    3778.3928270752654,
-    3495.851920450506,
-    3110.5580063983834,
-    2310.8518456156107,
-    2899.8918261585377,
-    3286.6881442816784,
-    3601.237076948981,
-    2590.9553048586554,
-    2555.2781425978933,
-    2004.8764850049579,
-    1333.8961665824775,
-    2090.217507623805,
-    0.363302046990876,
-    0.2506597877765041,
-    0.12741811820991292,
-    0.32195020821212794,
-    0.43317540002685884,
-    0.7725988160553472,
-    0.791227193907261,
-    0.5957799875116326,
-    0.5873844015441929,
-    0.35855586659016336,
-    0.7267512979672636,
-    0.7214172326166498,
-    1.0,
-    0.9089476753706724,
-    0.8226695360434777,
-    0.7208819781157673,
-    0.5152795489332506,
-    0.6711736481838434,
-    0.7782925265622518,
-    0.8648282061576593,
-    0.5787625095682526,
-    0.5752349727514727,
-    0.43456864805018935,
-    0.27668525082454587,
-    0.47414670304783574,
-    4138.929019101607,
-    64531.08183195824,
-    175620.92531477427,
-    22.847729696357412,
-    14.671691561018095,
-    0.07533558084489102,
-    12446.865764906175,
-    47945.287047950456,
-    2.8973185436828195,
-    10.774373017335268,
-    3.472192991899253,
-    -0.013941562889309035,
-    0.09672681097411825,
-    0.5067195499928454,
-    0.755407246398865,
-    0.03711810817384146,
-    11.154421806888552,
-    64500.8986854629
-  ],
-  "scaler_scale": [
-    1458.5456651154973,
-    1319.8585484401115,
-    798.8535944732339,
-    1467.8233720347457,
-    1637.8964913406842,
-    1330.3349975112737,
-    1391.430499849884,
-    1444.166940848846,
-    1630.948040054198,
-    1406.2203759964518,
-    1289.9699402243327,
-    1442.0533616965101,
-    1437.7214049715994,
-    1393.522474091575,
-    1468.6421185157626,
-    1449.3479990930084,
-    1293.2464048717598,
-    1331.2560392843097,
-    1326.1289536453178,
-    1357.3405110533047,
-    1452.4854193036483,
-    1348.4425883366337,
-    1318.1429721243371,
-    1059.93845215709,
-    1114.1647557935548,
-    0.2395898634701691,
-    0.21706962815914935,
-    0.13523106483202163,
-    0.23880331588910964,
-    0.24830003478347082,
-    0.1464527498295455,
-    0.15391677914992113,
-    0.18125664726966026,
-    0.2326879002599809,
-    0.23502163992653513,
-    0.13026800431597335,
-    0.15563022147466685,
-    1.0,
-    0.09922737602626737,
-    0.18291931318098986,
-    0.15401181704844932,
-    0.2143892844194339,
-    0.16856049162074294,
-    0.15902500893917185,
-    0.18285009098439925,
-    0.17264751056304276,
-    0.21090366624550771,
-    0.16802111677577075,
-    0.19264329284433157,
-    0.19589977001187556,
-    1437.7214049715994,
-    32602.413979370118,
-    95845.11969895993,
-    3.426376344472427,
-    3.408382770733738,
-    0.033353666248921464,
-    5505.629576226806,
-    25703.01200969283,
-    0.4599551450527747,
-    2.978321440052941,
-    0.3916581766443181,
-    0.06096090153067211,
-    0.07864618660494935,
-    0.0344984508436715,
-    0.17668176728315207,
-    0.18905119470509504,
-    5352.30503788098,
-    32297.31796957845
-  ],
-  "ridge_coef": [
-    7.4424310127566695,
-    13.345966730219576,
-    2.351840055857306,
-    6.088230738742203,
-    -10.030964629299273,
-    3.876136979406362,
-    -11.251608537526174,
-    16.84502390958064,
-    -2.093552796584439,
-    -5.784923711493545,
-    -6.67830546424787,
-    -4.654052249161928,
-    6.038218458133514,
-    9.82412450487401,
-    -6.200667839175651,
-    -0.3133364534713342,
-    -8.75036029102127,
-    12.785901861589027,
-    -3.7296377182327123,
-    6.546167384121816,
-    -4.984129287282208,
-    8.311396481777527,
-    -0.6248790895663127,
-    2.69008779623183,
-    12.996047839696784,
-    -2.2609944767610504,
-    -5.131537716982507,
-    0.3988922195665723,
-    -5.197736884253156,
-    4.556854888903703,
-    -0.8642438099006351,
-    6.327731485629085,
-    -5.157281763422745,
-    0.10691827520622764,
-    4.656962972053113,
-    3.2628870750114887,
-    4.033159141354671,
-    0.0,
-    -2.9206404009765268,
-    1.8683691849941264,
-    2.408006875407745,
-    7.250310827671452,
-    -3.97015207422554,
-    0.7316093212194048,
-    -3.459346094204882,
-    2.4407660203169255,
-    -2.872982666400644,
-    1.8797071977799857,
-    -1.3374700235689694,
-    -7.9533345474852295,
-    6.038063637368508,
-    1.615806581558555,
-    95785.62883805836,
-    0.12233606167692031,
-    -0.1515900264871255,
-    2.2023033069961873,
-    8.776787743985668,
-    -0.16714060634667535,
-    -2.751671223554021,
-    0.2511944267079865,
-    6.13561607395193,
-    2.85703108671782,
-    -0.11255626089468472,
-    -0.9017242341101542,
-    -0.627291200283328,
-    3.4664885582435883,
-    0.02591345630626686,
-    0.5530407299425606
-  ],
-  "ridge_intercept": 175620.9253147744,
-  "n_features": 68,
-  "noise_threshold": 15.0,
-  "contact_threshold": 20.0,
-  "ema_alpha": 0.9
-}

src-tauri/src/serial_core/basin_force_estimator.rs

@@ -1,397 +0,0 @@
-//! 7×12 柔性压力点阵力估计 - Rust 实现
-//!
-//! 与 Python `basin_feature_extractor.py` 完全对齐。
-//! 内嵌 `model_params.json`，对每帧 7×12 传感器数据提取 68 维特征并用
-//! StandardScaler + Ridge 回归估计法向力 Fz。
-
-use serde::Deserialize;
-
-// ───────────────── 常量 ─────────────────
-
-const ROWS: usize = 7;
-const COLS: usize = 12;
-const ROI_RADIUS: usize = 2;
-const ROI_SIZE: usize = 2 * ROI_RADIUS + 1; // 5
-const N_FEATURES: usize = 68; // 25 + 25 + 18
-
-// ───────────────── 模型参数 JSON（编译时嵌入）─────────────────
-
-const MODEL_PARAMS_JSON: &str = include_str!("../../resources/model_params.json");
-
-// ───────────────── 模型参数反序列化 ─────────────────
-
-#[derive(Debug, Deserialize)]
-struct ModelParams {
-    scaler_mean: Vec<f64>,
-    scaler_scale: Vec<f64>,
-    ridge_coef: Vec<f64>,
-    ridge_intercept: f64,
-    n_features: usize,
-    noise_threshold: f64,
-    contact_threshold: f64,
-    ema_alpha: f64,
-}
-
-// ───────────────── 估算器 ─────────────────
-
-pub struct BasinForceEstimator {
-    // 模型参数
-    scaler_mean: [f64; N_FEATURES],
-    scaler_scale: [f64; N_FEATURES],
-    ridge_coef: [f64; N_FEATURES],
-    ridge_intercept: f64,
-    // 超参数
-    noise_threshold: f64,
-    contact_threshold: f64,
-    ema_alpha: f64,
-    // 时序状态（需要可变）
-    prev_roi_sum: f64,
-    ema_sum: f64,
-    first_frame: bool,
-}
-
-impl BasinForceEstimator {
-    /// 使用编译时内嵌的 model_params.json 创建估算器
-    pub fn new() -> Self {
-        Self::from_json_str(MODEL_PARAMS_JSON)
-            .expect("内嵌 model_params.json 加载失败")
-    }
-
-    pub fn from_json_str(json: &str) -> Result<Self, Box<dyn std::error::Error>> {
-        let p: ModelParams = serde_json::from_str(json)?;
-        if p.n_features != N_FEATURES {
-            return Err(format!(
-                "模型特征维度不匹配: 期望 {}, 实际 {}",
-                N_FEATURES, p.n_features
-            )
-            .into());
-        }
-        let mut scaler_mean = [0.0; N_FEATURES];
-        let mut scaler_scale = [0.0; N_FEATURES];
-        let mut ridge_coef = [0.0; N_FEATURES];
-        scaler_mean.copy_from_slice(&p.scaler_mean);
-        scaler_scale.copy_from_slice(&p.scaler_scale);
-        ridge_coef.copy_from_slice(&p.ridge_coef);
-
-        Ok(Self {
-            scaler_mean,
-            scaler_scale,
-            ridge_coef,
-            ridge_intercept: p.ridge_intercept,
-            noise_threshold: p.noise_threshold,
-            contact_threshold: p.contact_threshold,
-            ema_alpha: p.ema_alpha,
-            prev_roi_sum: 0.0,
-            ema_sum: 0.0,
-            first_frame: true,
-        })
-    }
-
-    pub fn reset(&mut self) {
-        self.prev_roi_sum = 0.0;
-        self.ema_sum = 0.0;
-        self.first_frame = true;
-    }
-
-    pub fn predict_frame(&mut self, frame: &[f64; 84]) -> f64 {
-        let features = self.extract_features(frame);
-        self.ridge_predict(&features)
-    }
-
-    // ───────────── 特征提取 ─────────────
-
-    fn extract_features(&mut self, raw: &[f64; 84]) -> [f64; N_FEATURES] {
-        let mut x = [[0.0f64; COLS]; ROWS];
-        let mut max_value = 0.0f64;
-        for r in 0..ROWS {
-            for c in 0..COLS {
-                let v = raw[r * COLS + c].max(0.0);
-                x[r][c] = v;
-                if v > max_value {
-                    max_value = v;
-                }
-            }
-        }
-
-        if max_value < self.contact_threshold {
-            self.update_temporal(0.0);
-            return [0.0; N_FEATURES];
-        }
-
-        let mut peak_row = 0usize;
-        let mut peak_col = 0usize;
-        for r in 0..ROWS {
-            for c in 0..COLS {
-                if x[r][c] >= x[peak_row][peak_col] {
-                    peak_row = r;
-                    peak_col = c;
-                }
-            }
-        }
-
-        let roi = self.extract_roi(&x, peak_row, peak_col);
-        self.compute_features(&x, &roi, max_value, peak_row, peak_col)
-    }
-
-    fn extract_roi(
-        &self,
-        x: &[[f64; COLS]; ROWS],
-        pr: usize,
-        pc: usize,
-    ) -> [[f64; ROI_SIZE]; ROI_SIZE] {
-        let r = ROI_RADIUS as isize;
-        let mut roi = [[0.0f64; ROI_SIZE]; ROI_SIZE];
-
-        let r_start = (pr as isize - r).max(0) as usize;
-        let r_end = (pr + ROI_RADIUS + 1).min(ROWS);
-        let c_start = (pc as isize - r).max(0) as usize;
-        let c_end = (pc + ROI_RADIUS + 1).min(COLS);
-
-        let roi_r_start = (r_start as isize - (pr as isize - r)).max(0) as usize;
-        let roi_c_start = (c_start as isize - (pc as isize - r)).max(0) as usize;
-
-        for (i, ri) in (r_start..r_end).enumerate() {
-            for (j, ci) in (c_start..c_end).enumerate() {
-                roi[roi_r_start + i][roi_c_start + j] = x[ri][ci];
-            }
-        }
-        roi
-    }
-
-    fn compute_features(
-        &mut self,
-        x: &[[f64; COLS]; ROWS],
-        roi: &[[f64; ROI_SIZE]; ROI_SIZE],
-        max_value: f64,
-        peak_row: usize,
-        peak_col: usize,
-    ) -> [f64; N_FEATURES] {
-        let center = ROI_RADIUS;
-        let mut feat = [0.0f64; N_FEATURES];
-        let mut idx = 0;
-
-        // ROI 原始值 (25维)
-        for r in 0..ROI_SIZE {
-            for c in 0..ROI_SIZE {
-                feat[idx] = roi[r][c];
-                idx += 1;
-            }
-        }
-
-        // ROI 归一化形状 (25维)
-        for r in 0..ROI_SIZE {
-            for c in 0..ROI_SIZE {
-                feat[idx] = if max_value > 0.0 {
-                    roi[r][c] / max_value
-                } else {
-                    0.0
-                };
-                idx += 1;
-            }
-        }
-
-        // roi_sum, global_sum
-        let mut roi_sum = 0.0f64;
-        for r in 0..ROI_SIZE {
-            for c in 0..ROI_SIZE {
-                roi_sum += roi[r][c];
-            }
-        }
-        let mut global_sum = 0.0f64;
-        for r in 0..ROWS {
-            for c in 0..COLS {
-                global_sum += x[r][c];
-            }
-        }
-
-        // active_area
-        let thr = self.noise_threshold.max(0.05 * max_value);
-        let mut active_area = 0.0f64;
-        for r in 0..ROI_SIZE {
-            for c in 0..ROI_SIZE {
-                if roi[r][c] > thr {
-                    active_area += 1.0;
-                }
-            }
-        }
-
-        let participation = if max_value > 0.0 {
-            roi_sum / max_value
-        } else {
-            0.0
-        };
-        let concentration = if roi_sum > 0.0 {
-            max_value / roi_sum
-        } else {
-            0.0
-        };
-
-        // ring1_sum (上下左右4点)
-        let ring1_positions = [
-            (center - 1, center),
-            (center + 1, center),
-            (center, center - 1),
-            (center, center + 1),
-        ];
-        let ring1_sum: f64 = ring1_positions.iter().map(|&(r, c)| roi[r][c]).sum();
-
-        // ring2_sum (除中心和ring1外)
-        let mut ring2_sum = 0.0f64;
-        for r in 0..ROI_SIZE {
-            for c in 0..ROI_SIZE {
-                if (r, c) == (center, center) {
-                    continue;
-                }
-                if ring1_positions.contains(&(r, c)) {
-                    continue;
-                }
-                ring2_sum += roi[r][c];
-            }
-        }
-
-        let ring1_ratio = if max_value > 0.0 {
-            ring1_sum / max_value
-        } else {
-            0.0
-        };
-        let ring2_ratio = if max_value > 0.0 {
-            ring2_sum / max_value
-        } else {
-            0.0
-        };
-
-        // spread
-        let spread = if roi_sum > 0.0 {
-            let mut s = 0.0f64;
-            for r in 0..ROI_SIZE {
-                for c in 0..ROI_SIZE {
-                    let dr = r as f64 - center as f64;
-                    let dc = c as f64 - center as f64;
-                    s += (dr * dr + dc * dc) * roi[r][c];
-                }
-            }
-            s / roi_sum
-        } else {
-            0.0
-        };
-
-        // asym_x
-        let mut left_sum = 0.0f64;
-        let mut right_sum = 0.0f64;
-        for r in 0..ROI_SIZE {
-            for c in 0..center {
-                left_sum += roi[r][c];
-            }
-            for c in (center + 1)..ROI_SIZE {
-                right_sum += roi[r][c];
-            }
-        }
-        let asym_x = if roi_sum > 0.0 {
-            (right_sum - left_sum) / roi_sum
-        } else {
-            0.0
-        };
-
-        // asym_y
-        let mut up_sum = 0.0f64;
-        let mut down_sum = 0.0f64;
-        for r in 0..center {
-            for c in 0..ROI_SIZE {
-                up_sum += roi[r][c];
-            }
-        }
-        for r in (center + 1)..ROI_SIZE {
-            for c in 0..ROI_SIZE {
-                down_sum += roi[r][c];
-            }
-        }
-        let asym_y = if roi_sum > 0.0 {
-            (down_sum - up_sum) / roi_sum
-        } else {
-            0.0
-        };
-
-        // 位置
-        let peak_row_norm = peak_row as f64 / (ROWS - 1) as f64;
-        let peak_col_norm = peak_col as f64 / (COLS - 1) as f64;
-
-        // near_edge
-        let r = ROI_RADIUS as isize;
-        let near_edge = if (peak_row as isize) < r
-            || peak_row >= ROWS - ROI_RADIUS
-            || (peak_col as isize) < r
-            || peak_col >= COLS - ROI_RADIUS
-        {
-            1.0
-        } else {
-            0.0
-        };
-
-        // 时序特征
-        let delta_sum = roi_sum - self.prev_roi_sum;
-        if self.first_frame {
-            self.ema_sum = roi_sum;
-            self.first_frame = false;
-        } else {
-            self.ema_sum = self.ema_alpha * self.ema_sum + (1.0 - self.ema_alpha) * roi_sum;
-        }
-        self.prev_roi_sum = roi_sum;
-
-        let scalars = [
-            max_value,
-            roi_sum,
-            global_sum,
-            active_area,
-            participation,
-            concentration,
-            ring1_sum,
-            ring2_sum,
-            ring1_ratio,
-            ring2_ratio,
-            spread,
-            asym_x,
-            asym_y,
-            peak_row_norm,
-            peak_col_norm,
-            near_edge,
-            delta_sum,
-            self.ema_sum,
-        ];
-        for &v in &scalars {
-            feat[idx] = v;
-            idx += 1;
-        }
-
-        debug_assert_eq!(idx, N_FEATURES);
-        feat
-    }
-
-    fn update_temporal(&mut self, roi_sum: f64) {
-        self.prev_roi_sum = roi_sum;
-        if self.first_frame {
-            self.ema_sum = roi_sum;
-            self.first_frame = false;
-        } else {
-            self.ema_sum = self.ema_alpha * self.ema_sum + (1.0 - self.ema_alpha) * roi_sum;
-        }
-    }
-
-    // ───────────── 推理 ─────────────
-
-    fn ridge_predict(&self, features: &[f64; N_FEATURES]) -> f64 {
-        let mut scaled = [0.0f64; N_FEATURES];
-        for i in 0..N_FEATURES {
-            let s = self.scaler_scale[i];
-            scaled[i] = if s.abs() > 1e-12 {
-                (features[i] - self.scaler_mean[i]) / s
-            } else {
-                0.0
-            };
-        }
-        let mut y = self.ridge_intercept;
-        for i in 0..N_FEATURES {
-            y += self.ridge_coef[i] * scaled[i];
-        }
-        y
-    }
-}

src-tauri/src/serial_core/codecs/tactile_a.rs

@@ -12,7 +12,7 @@ use async_trait::async_trait;
 use csv::StringRecord;
 use anyhow::anyhow;
 use std::io::Read;
-use log::{debug, info};
+use log::debug;
 
 const FRAME_BUFFER_MIN_LENGTH: usize = 15;
 
@@ -226,7 +226,6 @@ impl Codec<TactileAFrame> for TactileACodec {
                 req_bytes.extend_from_slice((f.meta.except_data_len as u16).to_le_bytes().as_slice());
                 let checksum = calc_crc8_itu(req_bytes.as_slice());
                 req_bytes.push(checksum);
-                info!("send: {:02X?}", req_bytes);
                 Ok(req_bytes)
             }
             _ => {

src-tauri/src/serial_core/mod.rs

@@ -13,7 +13,6 @@ pub mod record;
 pub mod utils;
 #[cfg(feature = "multi-dim")]
 pub mod multi_dim_force;
-pub mod basin_force_estimator;
 
 pub type TestRecording = Recording<TestFrame>;
 pub type TactileARecording = Recording<TactileAFrame>;

src-tauri/src/serial_core/serial.rs

@@ -1,4 +1,3 @@
-use crate::serial_core::basin_force_estimator::BasinForceEstimator;
 use crate::serial_core::codec::Codec;
 use crate::serial_core::codecs::tactile_a::TactileACodec;
 use crate::serial_core::frame::{FrameHandler, TactileAFrame, TestFrame};
@@ -234,7 +233,6 @@ where
     let mut prune_interval = time::interval(Duration::from_millis(450));
     #[cfg(feature = "multi-dim")]
     let mut pzt_processor = PztProcessor::new();
-    let mut force_estimator = BasinForceEstimator::new();
     let mut pending_sub_frame: Option<PendingSubFrame<F>> = None;
     prune_interval.set_missed_tick_behavior(MissedTickBehavior::Delay);
 
@@ -311,16 +309,6 @@ where
                     drop(record);
 
                     if let Some(vals) = decode_res {
-                        // Basin force estimation (pre-force)
-                        if vals.len() == 84 {
-                            let mut frame_f64 = [0.0f64; 84];
-                            for (i, v) in vals.iter().enumerate() {
-                                frame_f64[i] = *v as f64;
-                            }
-                            let pre_force = force_estimator.predict_frame(&frame_f64);
-                            debug!("pre-force: {:.2}", pre_force);
-                        }
-
                         #[cfg(feature = "multi-dim")]
                         {
                             let pzt_values = vals.iter().map(|value| *value as f32).collect::<Vec<f32>>();
@@ -334,7 +322,6 @@ where
                             let force = raw_to_g1(summary as u32);
                             push_devkit_frame(&app, vals.as_slice(), frame.dts_ms(), force);
                         }
-
                         pending_sub_frame = Some(PendingSubFrame {
                             frame: frame.clone(),
                             values: vals,
@@ -410,12 +397,12 @@ fn infer_matrix_shape(len: usize) -> (u32, u32) {
 }
 
 fn raw_to_g1(raw: u32) -> f64 {
-    const X: [u32; 12] = [
+    const X: [u32; 11] = [
-        0, 84402, 117218, 140176, 159126, 175812, 191484, 208758, 224703, 252448, 302361, 352703,
+        0, 75507, 93732, 122031, 145263, 168630, 189980, 226021, 253636, 307140, 361368,
     ];
 
-    const Y: [f64; 12] = [
+    const Y: [f64; 11] = [
-        0.0, 160.0, 260.0, 360.0, 460.0, 560.0, 660.0, 760.0, 860.0, 1060.0, 1560.0, 2060.0,
+        0.0, 197.0, 257.0, 357.0, 457.0, 557.0, 657.0, 857.0, 1057.0, 1557.0, 2057.0,
     ];
 
     let n = X.len();

src-tauri/tauri.conf.json

@@ -23,7 +23,7 @@
     }
   },
   "bundle": {
-    "createUpdaterArtifacts": false,
+    "createUpdaterArtifacts": true,
     "active": true,
     "targets": "all",
     "icon": [

src/indicator/indicator498.py

@@ -1,747 +0,0 @@
-from typing import Dict, List, Tuple
-import numpy as np
-from indicator.base import Indicator
-import math
-
-
-class EMA:
-    """指数移动平均"""
-
-    @staticmethod
-    def calc(data: np.ndarray, period: int) -> np.ndarray:
-        alpha = 2.0 / (period + 1)
-        out = np.empty_like(data)
-        out[0] = data[0]
-        for i in range(1, len(data)):
-            out[i] = alpha * data[i] + (1 - alpha) * out[i - 1]
-        return out
-
-
-class SMA:
-    """简单移动平均"""
-
-    @staticmethod
-    def calc(data: np.ndarray, period: int) -> np.ndarray:
-        out = np.full_like(data, np.nan)
-        if len(data) < period:
-            return out
-        cumsum = np.cumsum(data)
-        out[period - 1:] = (cumsum[period - 1:] - np.concatenate([[0], cumsum[:-period]])) / period
-        return out
-
-
-def true_range(high: np.ndarray, low: np.ndarray, close: np.ndarray) -> np.ndarray:
-    """True Range"""
-    tr = np.empty(len(high))
-    tr[0] = high[0] - low[0]
-    for i in range(1, len(high)):
-        tr[i] = max(high[i] - low[i], abs(high[i] - close[i - 1]), abs(low[i] - close[i - 1]))
-    return tr
-
-
-class SignalTracker:
-    """逐帧更新的信号追踪器（无回看依赖，纯实时）"""
-
-    def __init__(
-        self,
-        ob_threshold: float = 80,
-        os_threshold: float = 20,
-        dist_extreme: float = 5,
-        dist_far: float = 15,
-        dist_mid: float = 30,
-        macd_th: float = 0.3,
-        rsi_ob: float = 70,
-        rsi_os: float = 30,
-        di_gap_bull: float = 15,
-        di_gap_bear: float = -15,
-        aroon_ob: float = 80,
-        aroon_os: float = 20,
-        williams_ob: float = -20,
-        williams_os: float = -80,
-        bias_ob: float = 5,
-        bias_os: float = -5,
-    ):
-        self.ob_threshold = ob_threshold
-        self.os_threshold = os_threshold
-        self.dist_extreme = dist_extreme
-        self.dist_far = dist_far
-        self.dist_mid = dist_mid
-        self.macd_th = macd_th
-        self.rsi_ob = rsi_ob
-        self.rsi_os = rsi_os
-        self.di_gap_bull = di_gap_bull
-        self.di_gap_bear = di_gap_bear
-        self.aroon_ob = aroon_ob
-        self.aroon_os = aroon_os
-        self.williams_ob = williams_ob
-        self.williams_os = williams_os
-        self.bias_ob = bias_ob
-        self.bias_os = bias_os
-
-        # 状态缓存
-        self._intp_buy_sma: float = 0.0
-        self._intp_sell_sma: float = 0.0
-        self._intp_sma_count: int = 0
-        self._max_qtb_score: float = 0.0
-        self._bull_count: int = 0
-        self._bear_count: int = 0
-        self._last_bull_idx: int = -100
-        self._last_bear_idx: int = -100
-        self._frame_idx: int = 0
-
-        # 历史缓冲区（保留最近200帧用于EMA/SMA/DI计算）
-        self._buf_size: int = 200
-        self._bbi_buf: List[float] = []
-        self._amplitude_buf: List[float] = []
-        self._close_buf: List[float] = []
-        self._high_buf: List[float] = []
-        self._low_buf: List[float] = []
-        self._wr_buf: List[float] = []
-
-    def reset(self):
-        """重置所有状态"""
-        self._intp_buy_sma = 0.0
-        self._intp_sell_sma = 0.0
-        self._intp_sma_count = 0
-        self._max_qtb_score = 0.0
-        self._bull_count = 0
-        self._bear_count = 0
-        self._last_bull_idx = -100
-        self._last_bear_idx = -100
-        self._frame_idx = 0
-        self._bbi_buf.clear()
-        self._amplitude_buf.clear()
-        self._close_buf.clear()
-        self._high_buf.clear()
-        self._low_buf.clear()
-        self._wr_buf.clear()
-
-    def _append_buf(self, buf: List[float], val: float):
-        buf.append(val)
-        if len(buf) > self._buf_size:
-            buf.pop(0)
-
-    def _ema_val(self, buf: List[float], period: int) -> float:
-        """返回缓冲区中最后一个EMA值"""
-        if len(buf) < period:
-            return buf[-1] if buf else 0.0
-        arr = np.array(buf, dtype=float)
-        ema = EMA.calc(arr, period)
-        return float(ema[-1])
-
-    def _sma_val(self, buf: List[float], period: int) -> float:
-        if len(buf) < period:
-            return np.nan
-        arr = np.array(buf, dtype=float)
-        sma = SMA.calc(arr, period)
-        return float(sma[-1])
-
-    def get_signals(
-        self,
-        amplitude: float,
-        bbi: float,
-        cci: float,
-        close: float,
-        dmk: float,
-        high: float,
-        k: float,
-        low: float,
-        qtb_score: float,
-        result: float,
-        wr: float,
-        percent: float,
-        v0: float,
-        boll_upper: float,
-        boll_lower: float,
-        macd_val: float,
-        pmacd: float,
-        bias: float,
-    ) -> Tuple[str, str, str, str]:
-        """
-        逐帧更新，返回 (market_status, signal_type, intensity, detail)
-
-        不依赖历史数组，所有指标值由外部实时计算后传入。
-        """
-
-        # ─── 更新缓冲区 ───
-        self._append_buf(self._bbi_buf, bbi)
-        self._append_buf(self._amplitude_buf, amplitude)
-        self._append_buf(self._close_buf, close)
-        self._append_buf(self._high_buf, high)
-        self._append_buf(self._low_buf, low)
-        self._append_buf(self._wr_buf, wr)
-
-        # ─── 指标因子计算（使用传入值 + 缓冲区衍生值）───
-        k_macd = self._k_macd_factor(k, macd_val, pmacd)
-        k_di = self._k_di_factor(dmk, amplitude, close)
-        k_aroon = self._k_aroon_factor(high, low, close)
-        k_williams = self._k_williams_factor(wr)
-        k_result = self._k_result_factor(result)
-        k_bias = self._k_bias_factor(bias)
-        k_trix = self._k_trix_factor(close)
-        k_ema = self._k_ema_factor(close)
-        k_amplitude = self._k_amplitude_factor(amplitude)
-
-        # ─── 累计 ───
-        k_bull = k_macd + k_di + k_aroon + k_williams + k_result + k_bias + k_trix + k_ema + k_amplitude
-        k_bear = k_macd + k_di + k_aroon + k_williams + k_result + k_bias + k_trix + k_ema + k_amplitude
-
-        if result > self.ob_threshold and k_bull > 0 and result >= 90:
-            k_bull *= 1.5
-
-        # ─── 指数平滑 ───
-        if self._intp_sma_count == 0:
-            self._intp_buy_sma = k_bull
-            self._intp_sell_sma = k_bear
-            self._intp_sma_count = 1
-        else:
-            self._intp_sma_count += 1
-            period = max(3, min(5, self._intp_sma_count))
-            alpha = 2.0 / (period + 1)
-            self._intp_buy_sma = alpha * k_bull + (1 - alpha) * self._intp_buy_sma
-            self._intp_sell_sma = alpha * k_bear + (1 - alpha) * self._intp_sell_sma
-
-        # ─── 趋势确认与背离检测 ───
-        is_bull_div, is_bear_div = self._detect_divergence(close, result)
-        if is_bull_div:
-            self._intp_buy_sma *= 1.3
-            k_bull *= 1.3
-        if is_bear_div:
-            self._intp_sell_sma *= 1.3
-            k_bear *= 1.3
-
-        is_bull, is_bear = self._detect_trend(close)
-        if is_bull:
-            self._intp_buy_sma *= 1.15
-            k_bull *= 1.15
-        if is_bear:
-            self._intp_sell_sma *= 1.15
-            k_bear *= 1.15
-
-        wmacd = self._wmacd()
-        if wmacd > 0:
-            k_bull += wmacd * 0.5
-        elif wmacd < 0:
-            k_bear += abs(wmacd) * 0.5
-
-        if qtb_score > self._max_qtb_score * 0.7:
-            pass
-        elif qtb_score < 3 and qtb_score > 1:
-            k_bull *= 0.85
-            k_bear *= 0.85
-
-        # ─── 信号分类 ───
-        max_k = max(abs(k_bull), abs(k_bear))
-        if max_k > 0:
-            buy_score = ((k_bull + max_k) / (2 * max_k)) * 100
-            sell_score = ((k_bear + max_k) / (2 * max_k)) * 100
-        else:
-            buy_score = sell_score = 50
-
-        bull_t = buy_score > 70 and result < 90
-        bear_t = sell_score > 70 and result > 10
-        k_cci = cci / 300
-        boll_mid = self._boll_mid()
-
-        is_bull_t = (
-            bull_t
-            and k_bull > k_cci
-            and close < boll_mid
-            and (
-                (result > 65 and result < 85 and k_bull > 0 and k_bear > 0 and (k_bear - k_bull < 1.5 or result > 75))
-                or (result < 35 and result > 15 and k_bear > 0 and k_bull > 0 and (k_bull - k_bear < 1.5 or result < 25))
-            )
-        )
-        is_bear_t = (
-            bear_t
-            and k_bear > k_cci
-            and close > boll_mid
-            and (
-                (result > 65 and result < 85 and k_bull > 0 and k_bear > 0 and (k_bear - k_bull < 1.5 or result > 75))
-                or (result < 35 and result > 15 and k_bear > 0 and k_bull > 0 and (k_bull - k_bear < 1.5 or result < 25))
-            )
-        )
-
-        if is_bull_t:
-            self._bull_count += 1
-            self._bear_count = 0
-            self._last_bull_idx = self._frame_idx
-        elif is_bear_t:
-            self._bear_count += 1
-            self._bull_count = 0
-            self._last_bear_idx = self._frame_idx
-        else:
-            self._bull_count = max(self._bull_count - 1, 0)
-            self._bear_count = max(self._bear_count - 1, 0)
-
-        # ─── 信号输出 ───
-        sig_strength = 1.0 + max(0, (self._bull_count - 3) * 0.1) + max(0, (self._bear_count - 3) * 0.1)
-
-        strength = (
-            "极强" if sig_strength >= 1.7
-            else "强" if sig_strength >= 1.4
-            else "中" if sig_strength >= 1.1
-            else "弱"
-        )
-
-        if buy_score >= sell_score:
-            signal = f"买{strength}"
-        else:
-            signal = f"卖{strength}"
-
-        # ─── 市场状态 ───
-        if abs(buy_score - sell_score) < 10:
-            status = "中性震荡"
-        elif buy_score > sell_score:
-            if self._bull_count >= 3:
-                status = "强势上涨"
-            elif result > self.ob_threshold:
-                status = "高位企稳"
-            else:
-                status = "温和上涨"
-        else:
-            if self._bear_count >= 3:
-                status = "强势下跌"
-            elif result < self.os_threshold:
-                status = "低位企稳"
-            else:
-                status = "温和下跌"
-
-        # ─── 距离 ───
-        boll_len = boll_upper - boll_lower
-        if boll_len > 0:
-            dist_ratio = (close - boll_lower) / boll_len * 100
-        else:
-            dist_ratio = 50
-
-        if dist_ratio < 50 - self.dist_extreme:
-            dist = "极端超卖"
-        elif dist_ratio < 50 - self.dist_far:
-            dist = "远离"
-        elif dist_ratio < 50 - self.dist_mid:
-            dist = "偏离"
-        elif dist_ratio < 50 + self.dist_mid:
-            dist = "接近"
-        elif dist_ratio < 50 + self.dist_far:
-            dist = "靠近"
-        elif dist_ratio < 50 + self.dist_extreme:
-            dist = "远超"
-        else:
-            dist = "极端超买"
-
-        # ─── 强度 ───
-        max_score = max(buy_score, sell_score)
-        intensity = (
-            "超强" if max_score >= 90
-            else "强" if max_score >= 80
-            else "中" if max_score >= 65
-            else "弱" if max_score >= 55
-            else "极弱"
-        )
-
-        # ─── 详情 ───
-        bias_val = self._ema_bias()
-        bbp_val = self._boll_pct_b(close, boll_upper, boll_lower)
-        detail = (
-            f"前量:{percent:.1f} 数量:{int(amplitude):03d} 百分比:{bbp_val:.1f} "
-            f"正:{k_bull:.1f} 负:{k_bear:.1f} 连买:{self._bull_count} 连卖:{self._bear_count} "
-            f"误差:{bias_val:.1f}"
-        )
-
-        self._frame_idx += 1
-        return status, signal, intensity, detail
-
-    # ═══════════════════════════════════════════════════
-    #  指标因子（全部基于实时数据，无回看窗口）
-    # ═══════════════════════════════════════════════════
-
-    def _k_macd_factor(self, k: float, macd_val: float, pmacd: float) -> float:
-        is_ob = k > self.ob_threshold
-        is_os = k < self.os_threshold
-        k_macd = 0.0
-        if is_ob or is_os:
-            if macd_val < 0:
-                if is_os and k < 30 and pmacd > 0 and pmacd <= 10 and macd_val > 1.5:
-                    k_macd = 3.5
-                elif is_ob and k > 80 and macd_val > 3 and abs(pmacd) < 5:
-                    k_macd = -3.5
-        elif pmacd < 0 and macd_val < 0:
-            if abs(pmacd) > 15 and macd_val >= -0.5:
-                k_macd = 3.5
-            elif 5 < abs(pmacd) < 15 and macd_val > 0.7:
-                k_macd = 3.5
-        elif pmacd > 0 and macd_val > 0:
-            if pmacd >= 15 and macd_val < 0.5:
-                k_macd = -3.5
-            elif 5 < pmacd < 15 and macd_val < -0.7:
-                k_macd = -3.5
-        elif macd_val >= 3:
-            k_macd = macd_val * 1.5
-        elif macd_val <= -3:
-            k_macd = macd_val * 1.5
-        return k_macd
-
-    def _k_di_factor(self, dmk: float, amplitude: float, close: float) -> float:
-        if len(self._close_buf) < 20:
-            return 0.0
-        k_close = np.array(self._close_buf, dtype=float)
-        period = 14
-        if len(k_close) < period + 1:
-            return 0.0
-        dx_list = []
-        for i in range(1, min(period + 1, len(k_close))):
-            diff = k_close[-i] - k_close[-i - 1]
-            dx_list.append(diff)
-        if not dx_list:
-            return 0.0
-        last_diff = dx_list[0]
-        adx_val = abs(dmk) * 0.5
-
-        k_di = 0.0
-        if adx_val < 20:
-            return 0.0
-        if dmk > 0 and len(dx_list) > 5:
-            gains = [d for d in dx_list[:5] if d > 0]
-            if len(gains) >= 3 and last_diff > 0:
-                k_di = min(adx_val / 5, 6.0)
-        elif dmk < 0 and len(dx_list) > 5:
-            losses = [d for d in dx_list[:5] if d < 0]
-            if len(losses) >= 3 and last_diff < 0:
-                k_di = -min(adx_val / 5, 6.0)
-        return k_di
-
-    def _k_aroon_factor(self, high: float, low: float, close: float) -> float:
-        period = 14
-        if len(self._high_buf) < period:
-            return 0.0
-        highs = self._high_buf[-period:]
-        lows = self._low_buf[-period:]
-        highest = max(highs)
-        lowest = min(lows)
-        rng = highest - lowest
-        if rng == 0:
-            return 0.0
-        k_aroon = 0.0
-        pct = (close - lowest) / rng * 100
-        if pct >= self.aroon_ob:
-            if close >= highest * 0.995:
-                k_aroon = -2.0
-            else:
-                k_aroon = 2.0
-        elif pct <= self.aroon_os:
-            if close <= lowest * 1.005:
-                k_aroon = 2.0
-            else:
-                k_aroon = -2.0
-        else:
-            k_aroon = (pct - 50) / 50 * 1.2
-        return k_aroon
-
-    def _k_williams_factor(self, wr: float) -> float:
-        k_wr = 0.0
-        if wr > self.williams_ob:
-            if wr > -10:
-                k_wr = -1.2
-            else:
-                k_wr = -0.8
-        elif wr < self.williams_os:
-            if wr < -90:
-                k_wr = 1.2
-            else:
-                k_wr = 0.8
-        return k_wr
-
-    def _k_result_factor(self, result: float) -> float:
-        k_result = 0.0
-        is_ob = result > self.ob_threshold
-        is_os = result < self.os_threshold
-        if is_os and result < 10:
-            k_result = 1.5
-        elif is_ob and result > 90:
-            k_result = -1.5
-        return k_result
-
-    def _k_bias_factor(self, bias: float) -> float:
-        k_bias = 0.0
-        if bias < self.bias_os and bias < -3:
-            k_bias = min(abs(bias) / 5, 2.5)
-        elif bias > self.bias_ob and bias > 3:
-            k_bias = -min(abs(bias) / 5, 2.5)
-        return k_bias
-
-    def _k_trix_factor(self, close: float) -> float:
-        ema3 = self._ema_val(self._close_buf, 3)
-        ema9 = self._ema_val(self._close_buf, 9)
-        if ema9 == 0:
-            return 0.0
-        trix = (ema3 - ema9) / ema9 * 100
-        k_trix = 0.0
-        if trix > 0:
-            k_trix = min(trix / 2, 3.0)
-        elif trix < 0:
-            k_trix = max(trix / 2, -3.0)
-        return k_trix
-
-    def _k_ema_factor(self, close: float) -> float:
-        ema9 = self._ema_val(self._close_buf, 9)
-        if ema9 == 0:
-            return 0.0
-        bias = (close - ema9) / ema9 * 100
-        k_ema = 0.0
-        if bias < -2:
-            k_ema = min(abs(bias) * 0.3, 2.0)
-        elif bias > 2:
-            k_ema = -min(abs(bias) * 0.3, 2.0)
-        return k_ema
-
-    def _k_amplitude_factor(self, amplitude: float) -> float:
-        if len(self._amplitude_buf) < 10:
-            return 0.0
-        buf = self._amplitude_buf[-10:]
-        mean = sum(buf) / len(buf)
-        if mean == 0:
-            return 0.0
-        ratio = (amplitude - mean) / mean
-        k_amp = 0.0
-        if ratio > 0.5:
-            k_amp = min(ratio * 1.5, 3.0)
-        elif ratio < -0.3:
-            k_amp = max(ratio * 1.5, -3.0)
-        return k_amp
-
-    def _detect_divergence(self, close: float, result: float) -> Tuple[bool, bool]:
-        """简化背离检测（基于累计计数）"""
-        is_bull_div = False
-        is_bear_div = False
-        if len(self._close_buf) > 30:
-            c30 = self._close_buf[-30]
-            if close > c30 * 1.05 and result < 50:
-                is_bull_div = True
-            elif close < c30 * 0.95 and result > 50:
-                is_bear_div = True
-        return is_bull_div, is_bear_div
-
-    def _detect_trend(self, close: float) -> Tuple[bool, bool]:
-        """简化趋势确认"""
-        is_bull = False
-        is_bear = False
-        if len(self._close_buf) > 20:
-            c20 = self._close_buf[-20]
-            if close > c20 * 1.05:
-                is_bull = True
-            elif close < c20 * 0.95:
-                is_bear = True
-        return is_bull, is_bear
-
-    def _wmacd(self) -> float:
-        """简化加权MACD（基于缓冲区）"""
-        if len(self._close_buf) < 12:
-            return 0.0
-        ema12 = self._ema_val(self._close_buf, 12)
-        ema26 = self._ema_val(self._close_buf, 26)
-        return ema12 - ema26
-
-    def _boll_mid(self) -> float:
-        if len(self._close_buf) < 20:
-            return self._close_buf[-1] if self._close_buf else 0.0
-        return sum(self._close_buf[-20:]) / 20
-
-    def _ema_bias(self) -> float:
-        if not self._close_buf:
-            return 0.0
-        ema5 = self._ema_val(self._close_buf, 5)
-        ema10 = self._ema_val(self._close_buf, 10)
-        if ema10 == 0:
-            return 0.0
-        return (ema5 - ema10) / ema10 * 1000
-
-    def _boll_pct_b(self, close: float, upper: float, lower: float) -> float:
-        boll_len = upper - lower
-        if boll_len == 0:
-            return 50.0
-        return (close - lower) / boll_len * 100
-
-
-def get_signals(indicator: Indicator) -> Tuple[str, str, str, str]:
-    """
-    基于技术指标生成交易信号（逐帧调用版）
-
-    参数：
-        indicator: Indicator 对象，包含所有实时指标值
-
-    返回：
-        tuple: (market_status, signal_type, intensity, detail)
-    """
-    # ─── 指标提取 ───
-    bbi = indicator.BBI
-    amplitude = indicator.AMPLITUDE
-    cci = indicator.CCI
-    close = indicator.CLOSE
-    dmk = indicator.DMK
-    high = indicator.HIGH
-    k = indicator.K
-    low = indicator.LOW
-    macd_val = indicator.MACD
-    ob = indicator.OB
-    os_ = indicator.OS
-    ovs = indicator.OVS
-    ovc = indicator.OVC
-    result = indicator.RESULT
-    wr = indicator.WR
-    percent = indicator.PERCENT
-    v0 = indicator.V0
-    boll_upper = indicator.BOLL_UP
-    boll_lower = indicator.BOLL_LO
-    bias = indicator.BIAS
-
-    # ─── 指标阈值 ───
-    ob_threshold = ob if ob > 0 else 80
-    os_threshold = os_ if os_ > 0 else 20
-    dist_extreme = ovc if ovc > 0 else 5
-    dist_far = ovs if ovs > 0 else 15
-    dist_mid = 30
-    macd_th = 0.3
-    rsi_ob = ovc if ovc > 0 else 70
-    rsi_os = ovs if ovs > 0 else 30
-    di_gap_bull = ovc if ovc > 0 else 15
-    di_gap_bear = -di_gap_bull
-    aroon_ob = ob_threshold
-    aroon_os = os_threshold
-    williams_ob = -20
-    williams_os = -80
-    bias_ob = 5
-    bias_os = -5
-
-    # ─── 指标因子 ───
-    pmacd = getattr(indicator, 'PMACD', macd_val)
-
-    k_macd = _k_macd_factor(ob_threshold, os_threshold, k, macd_val, pmacd)
-    k_di = _k_di_factor(dmk, amplitude, close)
-    k_aroon = _k_aroon_factor(aroon_ob, aroon_os, high, low, close)
-    k_williams = _k_williams_factor(williams_ob, williams_os, wr)
-    k_result = _k_result_factor(ob_threshold, os_threshold, result)
-    k_bias = _k_bias_factor(bias_ob, bias_os, bias)
-
-    # ─── K值累计 ───
-    k_bull = k_macd + k_di + k_aroon + k_williams + k_result + k_bias
-    k_bear = k_macd + k_di + k_aroon + k_williams + k_result + k_bias
-
-    if result > ob_threshold and k_bull > 0 and result >= 90:
-        k_bull *= 1.5
-
-    # ─── 强度计算 ───
-    max_k = max(abs(k_bull), abs(k_bear))
-    if max_k > 0:
-        buy_score = ((k_bull + max_k) / (2 * max_k)) * 100
-        sell_score = ((k_bear + max_k) / (2 * max_k)) * 100
-    else:
-        buy_score = sell_score = 50
-
-    # ─── 信号判断 ───
-    bull_t = buy_score > 70 and result < 90
-    bear_t = sell_score > 70 and result > 10
-
-    k_cci = cci / 300
-    boll_mid = indicator.BOLL_MID if hasattr(indicator, 'BOLL_MID') else (boll_upper + boll_lower) / 2
-    boll_len = boll_upper - boll_lower
-
-    is_bull_t = (
-        bull_t
-        and k_bull > k_cci
-        and close < boll_mid
-        and (
-            (result > 65 and result < 85 and k_bull > 0 and k_bear > 0 and (k_bear - k_bull < 1.5 or result > 75))
-            or (result < 35 and result > 15 and k_bear > 0 and k_bull > 0 and (k_bull - k_bear < 1.5 or result < 25))
-        )
-    )
-    is_bear_t = (
-        bear_t
-        and k_bear > k_cci
-        and close > boll_mid
-        and (
-            (result > 65 and result < 85 and k_bull > 0 and k_bear > 0 and (k_bear - k_bull < 1.5 or result > 75))
-            or (result < 35 and result > 15 and k_bear > 0 and k_bull > 0 and (k_bull - k_bear < 1.5 or result < 25))
-        )
-    )
-
-    # ─── 信号强度 ───
-    sig_strength = 1.0
-    if is_bull_t:
-        sig_strength += 0.3 + max(0, (result - 70) / 30)
-    elif is_bear_t:
-        sig_strength += 0.3 + max(0, (30 - result) / 30)
-
-    strength = (
-        "极强" if sig_strength >= 1.7
-        else "强" if sig_strength >= 1.4
-        else "中" if sig_strength >= 1.1
-        else "弱"
-    )
-
-    # ─── 信号类型 ───
-    if is_bull_t:
-        signal = f"买{strength}"
-    elif is_bear_t:
-        signal = f"卖{strength}"
-    else:
-        signal = "观望"
-
-    # ─── 市场状态 ───
-    if abs(buy_score - sell_score) < 10:
-        status = "中性震荡"
-    elif buy_score > sell_score:
-        if result > ob_threshold:
-            status = "高位企稳"
-        else:
-            status = "温和上涨"
-    else:
-        if result < os_threshold:
-            status = "低位企稳"
-        else:
-            status = "温和下跌"
-
-    # ─── 距离 ───
-    if boll_len > 0:
-        dist_ratio = (close - boll_lower) / boll_len * 100
-    else:
-        dist_ratio = 50
-
-    if dist_ratio < 50 - dist_extreme:
-        dist = "极端超卖"
-    elif dist_ratio < 50 - dist_far:
-        dist = "远离"
-    elif dist_ratio < 50 - dist_mid:
-        dist = "偏离"
-    elif dist_ratio < 50 + dist_mid:
-        dist = "接近"
-    elif dist_ratio < 50 + dist_far:
-        dist = "靠近"
-    elif dist_ratio < 50 + dist_extreme:
-        dist = "远超"
-    else:
-        dist = "极端超买"
-
-    # ─── 强度标签 ───
-    max_score = max(buy_score, sell_score)
-    intensity = (
-        "超强" if max_score >= 90
-        else "强" if max_score >= 80
-        else "中" if max_score >= 65
-        else "弱" if max_score >= 55
-        else "极弱"
-    )
-
-    # ─── 详情 ───
-    ema5 = indicator.EMA5 if hasattr(indicator, 'EMA5') else close
-    ema10 = indicator.EMA10 if hasattr(indicator, 'EMA10') else close
-    ema20 = indicator.EMA20 if hasattr(indicator, 'EMA20') else close
-    bias_val = (ema5 - ema10) / ema10 * 1000 if ema10 != 0 else 0
-
-    bbp_val = (close - boll_lower) / boll_len * 100 if boll_len > 0 else 50
-
-    detail = (
-        f"前量:{percent:.1f} 数量:{int(amplitude):03d} 百分比:{bbp_val:.1f} "
-        f"正:{k_bull:.1f} 负:{k_bear:.1f}"
-    )
-
-    return status, signal, intensity, detail

src/lib/components/CenterStage.svelte

@@ -68,6 +68,7 @@
   let replaySide: "left" | "right" = "right";
 
   const minRailScale = 0.2;
+  const resultantForceZeroThreshold = 0.1;
   const dispatch = createEventDispatcher<{
     configclose: void;
     replaytoggle: void;
@@ -81,7 +82,8 @@
   $: replaySide = summarySide === "left" ? "right" : "left";
   $: replayToggleButtonText = replayIsPlaying ? replayPauseLabel : replayPlayLabel;
   $: replayProgressPercent = Math.round(Math.min(1, Math.max(0, replayProgress)) * 100);
-  $: summaryCurveVisible = summary.points.length > 0 && summary.points.some((value) => Number.isFinite(value) && Math.abs(value) >= 0.0001);
+  $: summaryCurveVisible =
+    summary.latest != null && Number.isFinite(summary.latest) && summary.latest > resultantForceZeroThreshold;
   $: splitMatrixTitle = locale === "zh-CN" ? "数字矩阵" : "Matrix";
   $: splitMatrixHint = locale === "zh-CN" ? "实时压力数据 / 数字矩阵" : "Live pressure matrix";
 

src/lib/components/PressureMatrixViewer.svelte

@@ -145,6 +145,10 @@
       return "--";
     }
 
+    if (value === 0) {
+      return "0";
+    }
+
     return value.toFixed(1);
   }
 

src/routes/+page.svelte

@@ -169,6 +169,7 @@
   const summaryPointsPerSeries = 42;
   const signalRenderTickMs = 1200;
   const replayDefaultFrameMs = 40;
+  const resultantForceZeroThreshold = 0.1;
   const showSignalPanels = false;
   const mockToneCycle: SignalTone[] = ["cyan", "lime", "orange", "violet", "gold", "rose"];
 
@@ -707,6 +708,21 @@
     return new Array<number>(totalCells).fill(0);
   }
 
+  function shouldZeroPressureMatrix(summaryValue: HudSummary): boolean {
+    return summaryValue.latest != null && Number.isFinite(summaryValue.latest) && summaryValue.latest === 0;
+  }
+
+  function resolvePressureMatrixForSummary(
+    sourceMatrix: number[] | null,
+    summaryValue: HudSummary
+  ): number[] | null {
+    if (shouldZeroPressureMatrix(summaryValue)) {
+      return buildZeroMatrix();
+    }
+
+    return sourceMatrix;
+  }
+
   function resetReplayVisualState(): void {
     pressureMatrix = buildZeroMatrix();
     signalPanels = buildInactivePanels();
@@ -743,9 +759,10 @@
     replayCurrentIndex = safeIndex;
     replayHasDisplayedFrame = true;
     replayProgress = replayFrames.length > 1 ? safeIndex / (replayFrames.length - 1) : 1;
-    pressureMatrix = frameValuesToMatrix(replayFrames[safeIndex].values);
+    const nextSummary = buildReplaySummaryAt(safeIndex);
+    pressureMatrix = resolvePressureMatrixForSummary(frameValuesToMatrix(replayFrames[safeIndex].values), nextSummary);
     signalPanels = buildInactivePanels();
-    summary = buildReplaySummaryAt(safeIndex);
+    summary = nextSummary;
     hasSignalData = true;
   }
 
@@ -911,16 +928,37 @@
     };
   }
 
-  function isZeroLikeValue(value: number): boolean {
+  function normalizeResultantForce(value: number): number {
-    return !Number.isFinite(value) || Math.abs(value) < 0.0001;
+    if (!Number.isFinite(value)) {
+      return 0;
+    }
+
+    return value <= resultantForceZeroThreshold ? 0 : value;
   }
 
-  function shouldHideSummary(points: number[]): boolean {
+  function normalizeNullableResultantForce(value: number | null): number | null {
-    return points.length === 0 || points.every((value) => isZeroLikeValue(value));
+    return value == null ? null : normalizeResultantForce(value);
   }
 
   function normalizeSummary(summaryValue: HudSummary): HudSummary {
-    return shouldHideSummary(summaryValue.points) ? buildEmptySummary() : summaryValue;
+    if (summaryValue.points.length === 0) {
+      return {
+        ...summaryValue,
+        latest: normalizeNullableResultantForce(summaryValue.latest),
+        min: normalizeNullableResultantForce(summaryValue.min),
+        max: normalizeNullableResultantForce(summaryValue.max)
+      };
+    }
+
+    const points = summaryValue.points.map(normalizeResultantForce);
+
+    return {
+      ...summaryValue,
+      points,
+      latest: points[points.length - 1],
+      min: Math.min(...points),
+      max: Math.max(...points)
+    };
   }
 
   function buildSummary(points: number[], xValues: number[] = []): HudSummary {
@@ -928,7 +966,8 @@
       return buildEmptySummary();
     }
 
-    const resolvedXValues = points.map((_, index) => {
+    const normalizedPoints = points.map(normalizeResultantForce);
+    const resolvedXValues = normalizedPoints.map((_, index) => {
       const x = xValues[index];
       return Number.isFinite(x) ? Number(x) : index + 1;
     });
@@ -936,10 +975,10 @@
     return {
       label: "Resultant Force",
       xValues: resolvedXValues,
-      points,
+      points: normalizedPoints,
-      latest: points[points.length - 1],
+      latest: normalizedPoints[normalizedPoints.length - 1],
-      min: Math.min(...points),
+      min: Math.min(...normalizedPoints),
-      max: Math.max(...points)
+      max: Math.max(...normalizedPoints)
     };
   }
 
@@ -985,20 +1024,21 @@
     if (replayHasData) {
       return;
     }
+    const normalizedSummary = normalizeSummary(packet.summary);
     signalPanels = showSignalPanels ? packet.panels : buildInactivePanels();
-    if (packet.summary.points.length > 0) {
+    if (normalizedSummary.points.length > 0) {
       const nowSeconds = Math.round((Date.now() - sessionStartedAt) / 100) / 10;
-      const pointCount = packet.summary.points.length;
+      const pointCount = normalizedSummary.points.length;
       const spacing =
         pointCount > 1 ? Math.min(1.2, nowSeconds / Math.max(pointCount - 1, 1)) : 0;
       const startX = Math.max(0, nowSeconds - spacing * Math.max(pointCount - 1, 0));
-      const xValues = packet.summary.points.map((_, index) => Math.round((startX + index * spacing) * 10) / 10);
+      const xValues = normalizedSummary.points.map((_, index) => Math.round((startX + index * spacing) * 10) / 10);
-      summary = { ...packet.summary, xValues };
+      summary = { ...normalizedSummary, xValues };
     } else {
-      summary = packet.summary;
+      summary = normalizedSummary;
     }
-    pressureMatrix = packet.pressureMatrix;
+    pressureMatrix = resolvePressureMatrixForSummary(packet.pressureMatrix, normalizedSummary);
-    hasSignalData = signalPanels.length > 0 || packet.summary.points.length > 0;
+    hasSignalData = signalPanels.length > 0 || normalizedSummary.points.length > 0;
   }
 
   function clearHudPanels(): void {