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yanjie:main yanjie:customer-demo yanjie:silicone yanjie:android-main yanjie:sdk-dev yanjie:pre-module-dev yanjie:display yanjie:cli yanjie:dev
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compare: yanjie:sdk-dev
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yanjie:customer-demo yanjie:main yanjie:silicone yanjie:android-main yanjie:sdk-dev yanjie:pre-module-dev yanjie:display yanjie:cli yanjie:dev
yanjie:0.4.0 yanjie:0.3.1 yanjie:0.3.0 yanjie:0.2.0 yanjie:0.1.0

1 Commits
pre-module ... sdk-dev

Author SHA1 Message Date
lenn
50b3741b1a refactor: sdk-dev changes including serial_core refactor and eskin-finger-sdk 2026-05-12 17:14:58 +08:00
40 changed files with 274 additions and 35613 deletions
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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)

26695
devkit/build/je-skin-devkit-server/xref-je-skin-devkit-server.html
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BIN
devkit/dist/je-skin-devkit-server.exe vendored
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1
eskin-finger-sdk Submodule

Submodule eskin-finger-sdk added at 705375085f

2
package-lock.json generated
View File

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

2
src-tauri/.cargo/config.toml
View File

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

53
src-tauri/Cargo.lock generated
View File

@@ -8,10 +8,7 @@ version = "0.4.0"
dependencies = [
"anyhow",
"async-stream",
"async-trait",
"axum 0.8.9",
"chrono",
"crc",
"csv",
"dirs",
"eskin-finger-sdk",
@@ -19,7 +16,6 @@ dependencies = [
"futures-util",
"humantime",
"log",
"ndarray",
"prost",
"prost-types",
"protoc-bin-vendored",
@@ -1156,8 +1152,6 @@ dependencies = [
[[package]]
name = "eskin-finger-sdk"
version = "0.1.0"
source = "sparse+http://crates.huangyanjie.com/api/v1/crates/"
checksum = "341d54dbc70a0fb7cdd04162cdda6ab5735f9a4f717b1921b42c00e8afc37bb9"
dependencies = [
"chrono",
"crc",
@@ -2482,16 +2476,6 @@ version = "0.8.4"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "47e1ffaa40ddd1f3ed91f717a33c8c0ee23fff369e3aa8772b9605cc1d22f4c3"
[[package]]
name = "matrixmultiply"
version = "0.3.10"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a06de3016e9fae57a36fd14dba131fccf49f74b40b7fbdb472f96e361ec71a08"
dependencies = [
"autocfg",
"rawpointer",
]
[[package]]
name = "memchr"
version = "2.8.0"
@@ -2581,19 +2565,6 @@ version = "0.10.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "1d87ecb2933e8aeadb3e3a02b828fed80a7528047e68b4f424523a0981a3a084"
[[package]]
name = "ndarray"
version = "0.15.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "adb12d4e967ec485a5f71c6311fe28158e9d6f4bc4a447b474184d0f91a8fa32"
dependencies = [
"matrixmultiply",
"num-complex",
"num-integer",
"num-traits",
"rawpointer",
]
[[package]]
name = "ndk"
version = "0.9.0"
@@ -2659,30 +2630,12 @@ version = "0.1.14"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "72ef4a56884ca558e5ddb05a1d1e7e1bfd9a68d9ed024c21704cc98872dae1bb"
[[package]]
name = "num-complex"
version = "0.4.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
dependencies = [
"num-traits",
]
[[package]]
name = "num-conv"
version = "0.2.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "cf97ec579c3c42f953ef76dbf8d55ac91fb219dde70e49aa4a6b7d74e9919050"
[[package]]
name = "num-integer"
version = "0.1.46"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "7969661fd2958a5cb096e56c8e1ad0444ac2bbcd0061bd28660485a44879858f"
dependencies = [
"num-traits",
]
[[package]]
name = "num-traits"
version = "0.2.19"
@@ -3687,12 +3640,6 @@ version = "0.6.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "20675572f6f24e9e76ef639bc5552774ed45f1c30e2951e1e99c59888861c539"
[[package]]
name = "rawpointer"
version = "0.2.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "60a357793950651c4ed0f3f52338f53b2f809f32d83a07f72909fa13e4c6c1e3"
[[package]]
name = "redox_syscall"
version = "0.5.18"

9
src-tauri/Cargo.toml
View File

@@ -17,7 +17,6 @@ crate-type = ["staticlib", "cdylib", "rlib"]
[features]
default = []
devkit = ["dep:tonic", "dep:prost", "dep:prost-types", "dep:async-stream", "dep:dirs"]
multi-dim = ["dep:ndarray"]
[build-dependencies]
tauri-build = { version = "2", features = [] }
@@ -37,23 +36,19 @@ async-stream = { version = "0.3", optional = true }
dirs = { version = "6", optional = true }
tokio-serial = { version = "5.4.5" }
tokio = { version = "1.50.0", features = ["full"] }
async-trait = "0.1.89"
tokio-util = "0.7.18"
serde_json = "1"
fern = { version = "0.7.1", features = ["colored", "date-based"] }
log = "0.4.29"
humantime = "2.3.0"
csv = "1.4.0"
chrono = "0.4.44"
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" }
eskin-finger-sdk = { path = "../eskin-finger-sdk" }
[target.'cfg(not(any(target_os = "android", target_os = "ios")))'.dependencies]
tauri-plugin-updater = "2"

1021
src-tauri/nsis/installer.nsi
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File diff suppressed because it is too large Load Diff

217
src-tauri/resources/model_params.json
View File

@@ -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
}

133
src-tauri/src/commands/serial.rs
View File

@@ -1,27 +1,18 @@
use crate::serial_core::codecs::tactile_a::{
export_recording_csv, TactileACodec, TactileACsvImporter, TactileAHandler,
};
use crate::serial_core::error::SerialError;
use crate::serial_core::record::CsvImporter;
use crate::serial_core::serial::{PollMode, TactileAPollRequester};
use crate::serial_core::{serial, TactileARecording};
use crate::serial_core::record::{self, FingerRecording};
use crate::serial_core::serial;
use eskin_finger_sdk::device::EskinDevice;
use log::info;
use serde::Serialize;
use std::fs::File;
use std::io::Cursor;
use std::path::{Path, PathBuf};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};
use std::time::{SystemTime, UNIX_EPOCH};
use tauri::{async_runtime::JoinHandle, AppHandle, Manager, State};
use tokio_serial::{available_ports, SerialPortBuilderExt};
use tokio_serial::available_ports;
use tokio_util::sync::CancellationToken;
const DEFAULT_TACTILE_COLS: usize = 7;
const DEFAULT_TACTILE_ROWS: usize = 12;
const DEFAULT_TACTILE_POLL_INTERVAL_MS: u64 = 10;
const DEFAULT_TACTILE_REPLY_TIMEOUT_MS: u64 = 140;
type SharedTactileRecording = Arc<Mutex<TactileARecording>>;
type SharedRecording = Arc<Mutex<FingerRecording>>;
#[derive(Serialize)]
#[serde(rename_all = "camelCase")]
@@ -67,18 +58,18 @@ struct SerialSession {
port: String,
cancel: CancellationToken,
task: JoinHandle<()>,
current_record: SharedTactileRecording,
current_record: SharedRecording,
}
#[derive(Default)]
pub struct SerialConnectionState {
session: Mutex<Option<SerialSession>>,
last_record: Mutex<Option<SharedTactileRecording>>,
last_record: Mutex<Option<SharedRecording>>,
}
pub async fn shutdown_active_session(
state: &SerialConnectionState,
) -> Result<Option<(String, SharedTactileRecording)>, SerialError> {
) -> Result<Option<(String, SharedRecording)>, SerialError> {
let session = {
let mut guard = state.session.lock().map_err(|_| SerialError::StateError)?;
guard.take()
@@ -148,62 +139,41 @@ pub async fn serial_connect(
}
let cancel = CancellationToken::new();
let current_record = Arc::new(Mutex::new(TactileARecording::new()));
let current_record = Arc::new(Mutex::new(FingerRecording::new()));
let task_record = current_record.clone();
let task_cancel = cancel.clone();
let task_app = app.clone();
let task_port_name = port_name.clone();
let port = tokio_serial::new(&port_name, 921600)
.open_native_async()
.map_err(|_| SerialError::OpenError)?;
let session_started_at = Instant::now();
let task = tauri::async_runtime::spawn(async move {
let codec = TactileACodec::new(DEFAULT_TACTILE_COLS, DEFAULT_TACTILE_ROWS);
let handler = TactileAHandler;
let poll_mode = PollMode::Enabled(Box::new(TactileAPollRequester::new(
Duration::from_millis(DEFAULT_TACTILE_POLL_INTERVAL_MS),
DEFAULT_TACTILE_COLS,
DEFAULT_TACTILE_ROWS,
Duration::from_millis(DEFAULT_TACTILE_REPLY_TIMEOUT_MS),
)));
// Open device using SDK
let session = match serial::open_device(&task_port_name) {
Ok(s) => s,
Err(e) => {
eprintln!("Failed to open device: {e}");
cleanup_session(&task_app, &task_port_name, task_record).await;
return;
}
};
if let Err(error) = serial::run_serial_with_poll(
let mut device = session.device;
// Run stream with recording
if let Err(error) = serial::run_stream_with_record(
task_app.clone(),
port,
codec,
handler,
session_started_at,
task_record.clone(),
&mut device,
task_cancel,
poll_mode,
task_record.clone(),
)
.await
{
eprintln!("serial task exited with error: {error}");
}
let manager = task_app.state::<SerialConnectionState>();
if let Ok(mut last_record) = manager.last_record.lock() {
*last_record = Some(task_record);
}
// Close device
let _ = device.close();
let mut session = match manager.session.lock() {
Ok(session) => session,
Err(_) => return,
};
{
let should_clear = session
.as_ref()
.map(|current| current.port.as_str() == task_port_name.as_str())
.unwrap_or(false);
if should_clear {
session.take();
}
}
cleanup_session(&task_app, &task_port_name, task_record).await;
});
let mut session = state.session.lock().map_err(|_| SerialError::StateError)?;
@@ -227,6 +197,31 @@ pub async fn serial_connect(
})
}
async fn cleanup_session(
app: &AppHandle,
port_name: &str,
record: SharedRecording,
) {
let manager = app.state::<SerialConnectionState>();
if let Ok(mut last_record) = manager.last_record.lock() {
*last_record = Some(record);
}
let mut session = match manager.session.lock() {
Ok(session) => session,
Err(_) => return,
};
let should_clear = session
.as_ref()
.map(|current| current.port.as_str() == port_name)
.unwrap_or(false);
if should_clear {
session.take();
}
}
#[tauri::command]
pub async fn serial_disconnect(
state: State<'_, SerialConnectionState>,
@@ -293,8 +288,8 @@ pub fn serial_export_csv_to_path(
state: State<'_, SerialConnectionState>,
) -> Result<SerialExportResponse, SerialError> {
let output_path = resolve_export_path(file_path)?;
let record = resolve_record_for_export(&state)?;
let frame_count = write_record_to_csv(record, &output_path)?;
let rec = resolve_record_for_export(&state)?;
let frame_count = write_record_to_csv(rec, &output_path)?;
let path = output_path.display().to_string();
info!("csv exported to {path}, frame_count={frame_count}");
@@ -311,22 +306,20 @@ pub fn serial_import_csv(
file_name: String,
csv_content: String,
) -> Result<SerialImportResponse, SerialError> {
let mut importer = TactileACsvImporter::new(file_name.as_str());
let packets = importer
.load(Cursor::new(csv_content.into_bytes()))
let packets = record::import_csv(Cursor::new(csv_content.into_bytes()))
.map_err(|_| SerialError::ImportError)?;
if packets.is_empty() {
return Err(SerialError::NoRecordedData);
}
let channel_count = packets.first().map(|item| item.data.len()).unwrap_or(0);
let channel_count = 1; // fz is a single value per sample
let frame_count = packets.len();
let frames = packets
.into_iter()
.map(|packet| SerialImportFrame {
data: packet.data,
dts_ms: packet.dts_ms,
data: vec![packet.fz as i32],
dts_ms: packet.timestamp_us / 1000,
})
.collect();
@@ -355,7 +348,7 @@ pub fn serial_import_csv_from_path(file_path: String) -> Result<SerialImportResp
fn resolve_record_for_export(
state: &State<'_, SerialConnectionState>,
) -> Result<SharedTactileRecording, SerialError> {
) -> Result<SharedRecording, SerialError> {
let current_record = {
let session = state.session.lock().map_err(|_| SerialError::StateError)?;
session
@@ -406,7 +399,7 @@ fn snapshot_record_frame_count(
}
fn write_record_to_csv(
record: SharedTactileRecording,
record: SharedRecording,
output_path: &Path,
) -> Result<usize, SerialError> {
if let Some(parent) = output_path.parent() {
@@ -415,14 +408,14 @@ fn write_record_to_csv(
}
}
let mut file = File::create(output_path).map_err(|_| SerialError::ExportError)?;
let file = std::fs::File::create(output_path).map_err(|_| SerialError::ExportError)?;
let frame_count = {
let recording = record.lock().map_err(|_| SerialError::StateError)?;
if recording.frames.is_empty() {
return Err(SerialError::NoRecordedData);
}
export_recording_csv(&recording, &mut file).map_err(|_| SerialError::ExportError)?;
record::export_recording_csv(&recording, file).map_err(|_| SerialError::ExportError)?;
recording.frames.len()
};

397
src-tauri/src/serial_core/basin_force_estimator.rs
View File

@@ -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
}
}

6
src-tauri/src/serial_core/codec.rs
View File

@@ -1,6 +0,0 @@
use crate::serial_core::error::CodecError;
use std::time::Instant;
pub trait Codec<F> {
fn decode(&mut self, input: &[u8], session_started_at: Instant) -> Result<Vec<F>, CodecError>;
fn encode(&self, frame: &F) -> Result<Vec<u8>, CodecError>;
}

5
src-tauri/src/serial_core/codecs/mod.rs
View File

@@ -1,5 +0,0 @@
use crate::serial_core::{frame::TestFrame, record::Recording};
pub mod test;
pub mod tactile_a;
pub type TestRecording = Recording<TestFrame>;

383
src-tauri/src/serial_core/codecs/tactile_a.rs
View File

@@ -1,383 +0,0 @@
use crate::serial_core::error::CodecError;
use crate::serial_core::frame::{
FrameHandler, TactileAFrameMetaData, TactileARepFrame, TactileAReqFrame,
};
use crate::serial_core::record::{write_csv, CsvExporter, CsvImporter, RecordedFrame, Recording};
use crate::serial_core::utils::{calc_crc8_itu, elapsed_millis};
use crate::serial_core::{
codec::Codec,
frame::{TactileAFrame, TactileAFrameStatusCode},
};
use async_trait::async_trait;
use csv::StringRecord;
use anyhow::anyhow;
use std::io::Read;
use log::{debug, info};
const FRAME_BUFFER_MIN_LENGTH: usize = 15;
pub struct TactileACodec {
buffer: Vec<u8>,
expected_data_len: usize,
}
pub struct TactileACsvExporter {
channels: usize,
}
pub struct TactileACsvImporter {
channels: usize,
data_row: usize,
packets: Vec<TactileADataPacket>,
}
pub struct TactileAHandler;
#[derive(Clone)]
pub struct TactileADataPacket {
pub data: Vec<i32>,
pub dts_ms: u64,
}
impl From<u8> for TactileAFrameStatusCode {
fn from(value: u8) -> Self {
match value {
0 => TactileAFrameStatusCode::Success,
_ => TactileAFrameStatusCode::Failure,
}
}
}
impl TryFrom<&TactileARepFrame> for TactileADataPacket {
type Error = CodecError;
fn try_from(value: &TactileARepFrame) -> Result<TactileADataPacket, Self::Error> {
let data = TactileACodec::parse_data_frame(&value.payload)?;
let dts_ms = value.dts_ms;
Ok(TactileADataPacket {
data: data,
dts_ms: dts_ms,
})
}
}
impl TactileACodec {
pub fn new(cols: usize, rows: usize) -> TactileACodec {
Self {
buffer: Vec::new(),
expected_data_len: cols * rows * 2,
}
}
pub fn parse_data_frame(data: &[u8]) -> Result<Vec<i32>, CodecError> {
if data.len() % 2 != 0 {
return Err(CodecError::InvalidLength);
}
let vals: Vec<i32> = data
.chunks_exact(2)
.map(|chunk| {
let raw = u16::from_le_bytes([chunk[0], chunk[1]]) as i32;
if raw < 15 {
0
} else {
raw
}
})
.collect::<Vec<i32>>();
Ok(vals)
}
pub fn build_req_frame(cols: usize, rows: usize) -> anyhow::Result<TactileAFrame> {
let header = [0x55, 0xAA];
let payload_len: usize = 9;
let device_addr: u8 = 0x34;
let extend_code: u8 = 0x00;
let func_code: u8 = 0xFB;
let start_addr: u32 = 7168;
let except_data_len: usize = cols * rows * 2;
let checksum: u8 = 0;
Ok(TactileAFrame::Req(TactileAReqFrame {
meta: TactileAFrameMetaData {
header,
payload_len,
device_addr,
extend_code,
func_code,
start_addr,
except_data_len,
checksum,
},
}))
}
}
impl Codec<TactileAFrame> for TactileACodec {
fn decode(
&mut self,
input: &[u8],
session_started_at: std::time::Instant,
) -> Result<Vec<TactileAFrame>, CodecError> {
self.buffer.extend_from_slice(input);
let mut frames: Vec<TactileAFrame> = Vec::new();
loop {
if self.buffer.len() < FRAME_BUFFER_MIN_LENGTH {
break;
}
let header_pos = self.buffer.windows(2).position(|w| w == [0xAA, 0x55]);
let Some(pos) = header_pos else {
self.buffer.clear();
break;
};
if pos > 0 {
self.buffer.drain(0..pos);
}
if self.buffer.len() < FRAME_BUFFER_MIN_LENGTH {
break;
}
let header = [self.buffer[0], self.buffer[1]];
let payload_len = u16::from_le_bytes([self.buffer[2], self.buffer[3]]) as usize;
let device_addr = self.buffer[4];
let extend_code = self.buffer[5];
let func_code = self.buffer[6];
let start_addr = u32::from_le_bytes([
self.buffer[7],
self.buffer[8],
self.buffer[9],
self.buffer[10],
]);
let except_data_len = u16::from_le_bytes([self.buffer[11], self.buffer[12]]) as usize;
let status = TactileAFrameStatusCode::from(self.buffer[13]);
if except_data_len != self.expected_data_len {
debug!(
"unexpected payload length: expected {}, got {}, buffer_len={}",
self.expected_data_len,
except_data_len,
self.buffer.len()
);
self.buffer.drain(0..1);
continue;
}
let frame_length = except_data_len + FRAME_BUFFER_MIN_LENGTH;
if self.buffer.len() < frame_length {
break;
}
let need_check_data = self.buffer[0..14 + except_data_len].to_vec();
let payload = self.buffer[14..14 + except_data_len].to_vec();
let crc8_itu_alg = crc::Crc::<u8>::new(&crc::CRC_8_I_432_1);
let checksum = crc8_itu_alg.checksum(&need_check_data.as_slice());
if self.buffer[frame_length - 1] != checksum {
debug!(
"checksum mismatch: expected {:02X}, got {:02X}, frame_len={}",
checksum,
self.buffer[frame_length - 1],
frame_length
);
self.buffer.drain(0..1);
continue;
}
let dts_ms = elapsed_millis(session_started_at);
let meta: TactileAFrameMetaData = TactileAFrameMetaData {
header,
payload_len,
device_addr,
extend_code,
func_code,
start_addr,
except_data_len,
checksum,
};
frames.push(TactileAFrame::Rep({
TactileARepFrame {
meta,
status,
payload,
dts_ms,
}
}));
self.buffer.drain(0..frame_length);
}
Ok(frames)
}
fn encode(
&self,
frame: &TactileAFrame,
) -> Result<Vec<u8>, crate::serial_core::error::CodecError> {
match frame {
TactileAFrame::Req(f) => {
let mut req_bytes: Vec<u8> = Vec::new();
req_bytes.extend_from_slice(f.meta.header.as_slice());
req_bytes.extend_from_slice((f.meta.payload_len as u16).to_le_bytes().as_slice());
req_bytes.push(f.meta.device_addr);
req_bytes.push(f.meta.extend_code);
req_bytes.push(f.meta.func_code);
req_bytes.extend_from_slice(f.meta.start_addr.to_le_bytes().as_slice());
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)
}
_ => {
Err(CodecError::InvalidFrameType)
}
}
}
}
#[async_trait]
impl FrameHandler<TactileAFrame, i32> for TactileAHandler {
async fn on_frame(&mut self, frame: &TactileAFrame) -> anyhow::Result<Option<Vec<i32>>> {
match frame {
TactileAFrame::Rep(rep) => {
let vals = TactileACodec::parse_data_frame(&rep.payload)?;
Ok(Some(vals))
}
_ => Ok(None),
}
}
}
impl TactileACsvExporter {
fn new(channels: usize) -> Self {
TactileACsvExporter { channels }
}
}
impl CsvExporter<TactileARepFrame> for TactileACsvExporter {
type Error = CodecError;
fn csv_header(&self, _recording: &Recording<TactileARepFrame>) -> Vec<String> {
let mut header: Vec<String> = Vec::new();
for i in 0..self.channels {
header.push(format!("channel{}", i + 1));
}
header.push("dts".to_string());
header.push("summary".to_string());
header
}
fn csv_row(
&self,
item: &RecordedFrame<TactileARepFrame>,
) -> anyhow::Result<Vec<String>> {
let packet = TactileADataPacket::try_from(&item.frame)?;
let summary: i32 = packet.data.iter().sum();
let mut row: Vec<String> = packet.data.iter().map(|x| x.to_string()).collect();
row.push(packet.dts_ms.to_string());
row.push(summary.to_string());
Ok(row)
}
}
impl CsvExporter<TactileAFrame> for TactileACsvExporter {
type Error = CodecError;
fn csv_header(&self, _recording: &Recording<TactileAFrame>) -> Vec<String> {
let mut header: Vec<String> = Vec::new();
for i in 0..self.channels {
header.push(format!("channel{}", i + 1));
}
header.push("dts".to_string());
header
}
fn csv_row(
&self,
item: &RecordedFrame<TactileAFrame>,
) -> anyhow::Result<Vec<String>> {
let rep = match &item.frame {
TactileAFrame::Rep(rep) => rep,
TactileAFrame::Req(_) => return Err(anyhow!("request frame cannot be exported to csv row")),
};
let packet = TactileADataPacket::try_from(rep)?;
let mut row: Vec<String> = packet.data.iter().map(|x| x.to_string()).collect();
row.push(packet.dts_ms.to_string());
Ok(row)
}
}
impl TactileACsvImporter {
pub fn new(_path: &str) -> TactileACsvImporter {
Self {
channels: 0,
data_row: 0,
packets: Vec::new(),
}
}
fn parse_record(&mut self, record: StringRecord) -> anyhow::Result<TactileADataPacket> {
if self.channels == 0 {
return Err(anyhow!("csv header is missing channel columns"));
}
if record.len() < self.channels + 1 {
return Err(anyhow!("csv row has insufficient columns"));
}
let mut data = Vec::with_capacity(self.channels);
for index in 0..self.channels {
let cell = record.get(index).ok_or_else(|| anyhow!("missing channel cell"))?;
data.push(cell.parse::<i32>()?);
}
let dts_cell = record
.get(self.channels)
.ok_or_else(|| anyhow!("missing dts cell"))?;
let dts_ms = dts_cell.parse::<u64>()?;
Ok(TactileADataPacket {
data: data,
dts_ms: dts_ms,
})
}
}
impl CsvImporter<TactileADataPacket> for TactileACsvImporter {
fn load<R: Read>(&mut self, reader: R) -> anyhow::Result<Vec<TactileADataPacket>> {
let mut rdr = csv::Reader::from_reader(reader);
let headers = rdr.headers()?.clone();
self.channels = headers.len().saturating_sub(1);
self.data_row = 0;
self.packets.clear();
for record in rdr.records() {
let record = record?;
let packet = self.parse_record(record)?;
self.packets.push(packet);
self.data_row += 1;
}
Ok(self.packets.clone())
}
}
pub fn export_recording_csv<W>(recording: &Recording<TactileAFrame>, writer: W) -> anyhow::Result<()>
where
W: std::io::Write,
{
let channel_nb = recording
.frames
.iter()
.find_map(|frame| match &frame.frame {
TactileAFrame::Rep(rep) => Some(rep.payload.len() / 2),
TactileAFrame::Req(_) => None,
})
.unwrap_or(0);
let exporter = TactileACsvExporter::new(channel_nb);
write_csv(recording, &exporter, writer)
}

256
src-tauri/src/serial_core/codecs/test.rs
View File

@@ -1,256 +0,0 @@
use std::io::Read;
use std::time::Instant;
use crate::serial_core::frame::{FrameHandler};
use crate::serial_core::{codec::Codec, error::CodecError, frame::TestFrame};
use anyhow::anyhow;
use async_trait::async_trait;
use csv::StringRecord;
use crate::serial_core::record::{write_csv, CsvExporter, CsvImporter, RecordedFrame, Recording};
use crate::serial_core::utils::{
elapsed_millis,
usize_to_u16_be_bytes
};
pub struct TestCodec {
buffer: Vec<u8>,
}
pub struct TestHandler;
impl TestCodec {
pub fn new() -> TestCodec {
Self { buffer: Vec::new() }
}
}
impl Codec<TestFrame> for TestCodec {
fn decode(&mut self, input: &[u8], session_started_at: Instant) -> Result<Vec<TestFrame>, CodecError> {
self.buffer.extend_from_slice(input);
let mut frames = Vec::new();
loop {
if self.buffer.len() < 6 {
break;
}
let header_pos = self.buffer.windows(2).position(|w| w == [0xAA, 0x55]);
let Some(pos) = header_pos else {
self.buffer.clear();
break;
};
if pos > 0 {
self.buffer.drain(0..pos);
}
if self.buffer.len() < 6 {
break;
}
let cmd = self.buffer[2];
let length_bytes = [self.buffer[3], self.buffer[4]];
let length = u16::from_be_bytes(length_bytes) as usize;
let frame_length = (length + 6) as usize;
if self.buffer.len() < frame_length {
break;
}
let payload = self.buffer[5..5 + length].to_vec();
// let checksum = crc8(payload.as_slice());
let crc8_alg = crc::Crc::<u8>::new(&crc::CRC_8_SMBUS);
let checksum = crc8_alg.checksum(payload.as_slice());
if self.buffer[frame_length - 1] != checksum {
self.buffer.drain(0..1);
continue;
}
let dts = elapsed_millis(session_started_at);
println!("dts_ms: {dts}");
frames.push(TestFrame {
header: [0xAA, 0x55],
cmd: cmd,
length: length,
payload: payload,
checksum: checksum,
dts_ms: dts,
});
self.buffer.drain(0..frame_length);
}
Ok(frames)
}
fn encode(&self, frame: &TestFrame) -> Result<Vec<u8>, CodecError> {
let _ = u16::try_from(frame.payload.len()).map_err(|_| CodecError::PayloadTooLarge)?;
let mut out = Vec::with_capacity(6 + frame.length);
out.extend_from_slice(&frame.header);
out.push(frame.cmd);
out.extend_from_slice(&usize_to_u16_be_bytes(frame.length));
out.extend_from_slice(&frame.payload);
out.push(frame.checksum);
Ok(out)
}
}
#[async_trait]
impl FrameHandler<TestFrame, i32> for TestHandler {
async fn on_frame(&mut self, frame: &TestFrame) -> anyhow::Result<Option<Vec<i32>>> {
match frame.cmd {
0x01 => {
let vals = parse_data_frame(&frame.payload)?;
Ok(Some(vals))
}
_ => Ok(None),
}
}
}
fn parse_data_frame(data: &[u8]) -> Result<Vec<i32>, CodecError> {
if data.len() % 2 != 0 {
return Err(CodecError::InvalidLength);
}
let vals: Vec<i32> = data
.chunks_exact(2)
.map(|chunk| u16::from_be_bytes([chunk[0], chunk[1]]) as i32)
.collect::<Vec<i32>>();
Ok(vals)
}
pub struct TestCsvExporter;
pub struct TestCsvImporter {
channels: usize,
data_row: usize,
packets: Vec<TestDataPacket>,
}
#[derive(Clone)]
pub struct TestDataPacket {
pub data: Vec<i32>,
pub dts_ms: u64
}
impl TryFrom<&TestFrame> for TestDataPacket {
type Error = CodecError;
fn try_from(frame: &TestFrame) -> Result<TestDataPacket, Self::Error> {
let data = parse_data_frame(&frame.payload)?;
let dts = frame.dts_ms;
Ok(TestDataPacket { data: data, dts_ms: dts })
}
}
// impl From<TestFrame> for TestDataPacket {
// fn from(frame: TestFrame) -> Self {
// let data = parse_data_frame(&frame.payload)?;
// let dts = frame.dts_ms;
// TestDataPacket { data: data, dts_ms: dts }
// }
// }
impl CsvExporter<TestFrame> for TestCsvExporter {
type Error = CodecError;
fn csv_header(&self, recording: &Recording<TestFrame>) -> Vec<String> {
let channel_nb = recording
.frames
.iter()
.find_map(|frame| parse_data_frame(&frame.frame.payload).ok().map(|vals| vals.len()))
.unwrap_or(0);
let mut header: Vec<String> = Vec::new();
for i in 0..channel_nb {
header.push(format!("channel{}", i + 1));
}
header.push("dts".to_string());
header
}
fn csv_row(&self, item: &RecordedFrame<TestFrame>) -> anyhow::Result<Vec<String>> {
let packet: TestDataPacket = TestDataPacket::try_from(&item.frame)?;
let mut row: Vec<String> = packet.data.iter().map(|&x| x.to_string()).collect();
row.push(packet.dts_ms.to_string());
Ok(row)
}
}
impl TestCsvImporter {
pub fn new(_path: &str) -> TestCsvImporter {
Self {
channels: 0,
data_row: 0,
packets: Vec::new(),
}
}
fn parse_record(&mut self, record: StringRecord) -> anyhow::Result<TestDataPacket>{
if self.channels == 0 {
return Err(anyhow!("csv header is missing channel columns"));
}
if record.len() < self.channels + 1 {
return Err(anyhow!("csv row has insufficient columns"));
}
let mut data = Vec::with_capacity(self.channels);
for index in 0..self.channels {
let cell = record.get(index).ok_or_else(|| anyhow!("missing channel cell"))?;
data.push(cell.parse::<i32>()?);
}
let dts_cell = record
.get(self.channels)
.ok_or_else(|| anyhow!("missing dts cell"))?;
let dts_ms = dts_cell.parse::<u64>()?;
Ok(TestDataPacket {
data: data,
dts_ms: dts_ms,
})
}
}
impl CsvImporter<TestDataPacket> for TestCsvImporter {
fn load<R: Read>(&mut self, reader: R) -> anyhow::Result<Vec<TestDataPacket>> {
let mut rdr = csv::Reader::from_reader(reader);
let headers = rdr.headers()?.clone();
self.channels = headers.len().saturating_sub(1);
self.data_row = 0;
self.packets.clear();
for record in rdr.records() {
let record = record?;
let packet = self.parse_record(record)?;
self.packets.push(packet);
self.data_row += 1;
}
Ok(self.packets.clone())
}
}
pub fn export_recording_csv<W>(recording: &Recording<TestFrame>, writer: W) -> anyhow::Result<()>
where
W: std::io::Write,
{
write_csv(recording, &TestCsvExporter, writer)
}
#[cfg(test)]
mod tests {
use super::*;
use csv::Reader;
use std::io::Cursor;
#[test]
fn test_read_csv_basic() -> anyhow::Result<()> {
let mut rdr = Reader::from_path("recording_20260329_125238.csv")?;
let headers = rdr.headers()?;
println!("headers: {:?}", headers);
for result in rdr.records() {
let record = result?;
println!("record: {:?}", record);
}
Ok(())
}
}

57
src-tauri/src/serial_core/frame.rs
View File

@@ -1,57 +0,0 @@
use anyhow::Result;
use async_trait::async_trait;
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct TestFrame {
pub header: [u8; 2],
pub cmd: u8,
pub length: usize,
pub payload: Vec<u8>,
pub checksum: u8,
pub dts_ms: u64
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct TactileAFrameMetaData {
pub header: [u8; 2],
pub payload_len: usize,
pub device_addr: u8,
pub extend_code: u8,
pub func_code: u8,
pub start_addr: u32,
pub except_data_len: usize,
// pub status: u8,
// pub payload_data: Vec<u8>,
pub checksum: u8,
// pub dts_ms: u64,
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct TactileAReqFrame {
pub meta: TactileAFrameMetaData,
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct TactileARepFrame {
pub meta: TactileAFrameMetaData,
pub status: TactileAFrameStatusCode,
pub payload: Vec<u8>,
pub dts_ms: u64
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TactileAFrameStatusCode {
Success,
Failure
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TactileAFrame {
Req(TactileAReqFrame),
Rep(TactileARepFrame)
}
#[async_trait]
pub trait FrameHandler<F, T>: Send {
async fn on_frame(&mut self, frame: &F) -> Result<Option<Vec<T>>>;
}

31
src-tauri/src/serial_core/mod.rs
View File

@@ -1,35 +1,4 @@
use crate::serial_core::{
frame::{TactileAFrame, TestFrame},
record::Recording,
};
pub mod codec;
pub mod codecs;
pub mod error;
pub mod frame;
pub mod model;
pub mod serial;
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>;
pub struct SerialConnection {
pub port: String,
}
pub fn connect(port: &str) -> Result<SerialConnection, String> {
let port = port.trim();
if port.is_empty() {
return Err("Serial port is required".to_string());
}
Ok(SerialConnection {
port: port.to_string(),
})
}

335
src-tauri/src/serial_core/model.rs
View File

@@ -1,8 +1,6 @@
use crate::serial_core::frame::TestFrame;
use std::collections::HashMap;
use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};
const MAX_POINTS: usize = 28;
const MAX_SUMMARY_POINTS: usize = 42;
const PANEL_STALE_AFTER: Duration = Duration::from_millis(2400);
@@ -74,16 +72,6 @@ pub struct HudSignalIcon {
pub tone: HudTone,
}
struct HudPanelUpdate {
source_id: String,
values: Vec<f32>,
}
struct PanelEntry {
panel: HudSignalPanel,
last_seen: Instant,
}
pub struct HudChartState {
panels: HashMap<String, PanelEntry>,
order: Vec<String>,
@@ -92,6 +80,11 @@ pub struct HudChartState {
last_frame_seen: Option<Instant>,
}
struct PanelEntry {
panel: HudSignalPanel,
last_seen: Instant,
}
impl HudChartState {
pub fn new() -> Self {
Self {
@@ -105,76 +98,21 @@ impl HudChartState {
pub fn record_summary(&mut self, value: f32) {
push_summary_point(&mut self.summary_points, value);
self.last_frame_seen = Some(Instant::now());
}
pub fn record_pressure_matrix(&mut self, values: &[i32]) {
pub fn record_pressure_matrix(&mut self, values: &[f32]) {
if values.is_empty() {
return;
}
self.pressure_matrix = Some(values.iter().map(|value| *value as f32).collect());
}
pub fn apply_frame(&mut self, frame: &TestFrame, decoded_values: Option<&[i32]>) -> HudPacket {
let now = Instant::now();
self.last_frame_seen = Some(now);
for update in expand_frame_updates(frame, decoded_values) {
self.apply_update(update, now);
}
self.prune_stale_at(now);
self.snapshot()
self.pressure_matrix = Some(values.to_vec());
}
pub fn prune_stale(&mut self) -> Option<HudPacket> {
let now = Instant::now();
let before = self.panels.len();
let summary_points_before = self.summary_points.len();
self.prune_stale_at(Instant::now());
let summary_before = self.summary_points.len();
if before == self.panels.len() && summary_points_before == self.summary_points.len() {
return None;
}
Some(self.snapshot())
}
fn apply_update(&mut self, update: HudPanelUpdate, now: Instant) {
if update.values.is_empty() {
return;
}
if !self.panels.contains_key(&update.source_id) {
let next_side = side_for_index(self.order.len());
self.order.push(update.source_id.clone());
self.panels.insert(
update.source_id.clone(),
PanelEntry {
panel: build_panel(&update.source_id, next_side, update.values.len()),
last_seen: now,
},
);
}
let entry = self
.panels
.get_mut(&update.source_id)
.expect("panel entry should exist after insertion");
entry.last_seen = now;
entry.panel.active = true;
ensure_panel_channels(&mut entry.panel, update.values.len());
for (index, value) in update.values.into_iter().enumerate() {
if let Some(series) = entry.panel.series.get_mut(index) {
push_point(&mut series.points, value);
}
}
refresh_panel_stats(&mut entry.panel);
}
fn prune_stale_at(&mut self, now: Instant) {
self.panels
.retain(|_, entry| now.duration_since(entry.last_seen) <= PANEL_STALE_AFTER);
self.order.retain(|id| self.panels.contains_key(id));
@@ -189,6 +127,16 @@ impl HudChartState {
self.pressure_matrix = None;
self.last_frame_seen = None;
}
if before == self.panels.len() && summary_before == self.summary_points.len() {
return None;
}
Some(self.snapshot())
}
pub fn build_snapshot(&mut self) -> HudPacket {
self.snapshot()
}
fn snapshot(&mut self) -> HudPacket {
@@ -223,106 +171,6 @@ impl Default for HudChartState {
}
}
fn build_panel(source_id: &str, side: HudPanelSide, channel_count: usize) -> HudSignalPanel {
HudSignalPanel {
id: format!("panel-{source_id}"),
code: source_id.to_string(),
title: format!("Source {source_id}"),
side,
active: true,
series: build_panel_series(source_id, channel_count, &[]),
icons: build_panel_icons(source_id, channel_count),
latest: None,
min: None,
max: None,
}
}
fn expand_frame_updates(frame: &TestFrame, decoded_values: Option<&[i32]>) -> Vec<HudPanelUpdate> {
if let Some(values) = decoded_values {
if values.is_empty() {
return Vec::new();
}
return vec![HudPanelUpdate {
source_id: format_source_id(frame.cmd),
values: values.iter().map(|value| *value as f32).collect(),
}];
}
let chunks = frame.payload.chunks_exact(4);
if !frame.payload.is_empty() && chunks.remainder().is_empty() {
return chunks.map(build_update_from_chunk).collect();
}
vec![HudPanelUpdate {
source_id: format_source_id(frame.cmd),
values: fallback_values(frame),
}]
}
fn build_update_from_chunk(chunk: &[u8]) -> HudPanelUpdate {
HudPanelUpdate {
source_id: format_source_id(chunk[0]),
values: chunk[1..]
.iter()
.enumerate()
.map(|(index, byte)| normalize_value(*byte, tone_for_index(index)))
.collect(),
}
}
fn fallback_values(frame: &TestFrame) -> Vec<f32> {
let mut bytes = frame.payload.clone();
if bytes.is_empty() {
bytes.extend([
frame.cmd,
frame.length as u8,
frame.checksum,
frame.cmd.wrapping_add(frame.checksum),
]);
}
while bytes.len() < 3 {
let previous = *bytes.last().unwrap_or(&frame.cmd);
bytes.push(
previous
.wrapping_add(frame.cmd)
.wrapping_add(bytes.len() as u8),
);
}
bytes
.into_iter()
.enumerate()
.map(|(index, byte)| normalize_value(byte, tone_for_index(index)))
.collect()
}
fn normalize_value(byte: u8, tone: HudTone) -> f32 {
let base = (byte as f32 / 255.0) * 100.0;
let offset = match tone {
HudTone::Cyan => 6.0,
HudTone::Lime => 0.0,
HudTone::Orange => -6.0,
HudTone::Violet => 10.0,
HudTone::Gold => -10.0,
HudTone::Rose => 3.0,
};
(base + offset).clamp(0.0, 100.0)
}
fn format_source_id(byte: u8) -> String {
if byte.is_ascii_alphanumeric() {
(byte as char).to_ascii_uppercase().to_string()
} else {
format!("CH{:02X}", byte)
}
}
fn side_for_index(index: usize) -> HudPanelSide {
if index % 2 == 0 {
HudPanelSide::Left
@@ -331,91 +179,6 @@ fn side_for_index(index: usize) -> HudPanelSide {
}
}
fn push_point(points: &mut Vec<f32>, value: f32) {
if points.len() >= MAX_POINTS {
points.remove(0);
}
points.push((value * 10.0).round() / 10.0);
}
fn build_panel_series(
source_id: &str,
channel_count: usize,
previous: &[HudSignalSeries],
) -> Vec<HudSignalSeries> {
(0..channel_count)
.map(|index| HudSignalSeries {
id: format!("{source_id}-series-{}", index + 1),
tone: tone_for_index(index),
points: previous
.get(index)
.map(|series| series.points.clone())
.unwrap_or_default(),
})
.collect()
}
fn build_panel_icons(source_id: &str, channel_count: usize) -> Vec<HudSignalIcon> {
(0..channel_count)
.map(|index| HudSignalIcon {
id: format!("{source_id}-icon-{}", index + 1),
label: if channel_count == 1 {
"TOTAL".to_string()
} else {
format!("{source_id}-{}", index + 1)
},
tone: tone_for_index(index),
})
.collect()
}
fn ensure_panel_channels(panel: &mut HudSignalPanel, channel_count: usize) {
if panel.series.len() == channel_count && panel.icons.len() == channel_count {
return;
}
panel.series = build_panel_series(&panel.code, channel_count, &panel.series);
panel.icons = build_panel_icons(&panel.code, channel_count);
}
fn refresh_panel_stats(panel: &mut HudSignalPanel) {
let latest_values: Vec<f32> = panel
.series
.iter()
.filter_map(|series| series.points.last().copied())
.collect();
panel.latest = if latest_values.is_empty() {
None
} else {
Some(latest_values.iter().sum::<f32>() / latest_values.len() as f32)
};
panel.min = panel
.series
.iter()
.flat_map(|series| series.points.iter().copied())
.reduce(f32::min);
panel.max = panel
.series
.iter()
.flat_map(|series| series.points.iter().copied())
.reduce(f32::max);
}
fn tone_for_index(index: usize) -> HudTone {
match index % 6 {
0 => HudTone::Cyan,
1 => HudTone::Lime,
2 => HudTone::Orange,
3 => HudTone::Violet,
4 => HudTone::Gold,
_ => HudTone::Rose,
}
}
fn push_summary_point(points: &mut Vec<f32>, value: f32) {
if points.len() >= MAX_SUMMARY_POINTS {
points.remove(0);
@@ -440,61 +203,3 @@ fn now_millis() -> u64 {
.map(|duration| duration.as_millis() as u64)
.unwrap_or_default()
}
// #[cfg(test)]
// mod tests {
// use super::*;
//
// fn sample_frame() -> TestFrame {
// TestFrame {
// header: [0xAA, 0x55],
// cmd: 0x01,
// length: 4,
// payload: vec![0x00, 0x0A, 0x00, 0x14],
// checksum: 0,
//
// }
// }
//
// #[test]
// fn prune_stale_clears_panels_and_summary_after_timeout() {
// let mut state = HudChartState::new();
// let frame = sample_frame();
//
// state.record_summary(30.0);
// let _ = state.apply_frame(&frame, Some(&[10, 20]));
//
// let stale_now = Instant::now();
// let stale_seen = stale_now - PANEL_STALE_AFTER - Duration::from_millis(1);
//
// state.last_frame_seen = Some(stale_seen);
//
// for entry in state.panels.values_mut() {
// entry.last_seen = stale_seen;
// }
//
// let packet = state
// .prune_stale()
// .expect("stale data should emit an update");
//
// assert!(packet.panels.is_empty());
// assert!(packet.summary.points.is_empty());
// assert!(state.panels.is_empty());
// assert!(state.summary_points.is_empty());
// }
//
// #[test]
// fn prune_stale_keeps_recent_summary_points() {
// let mut state = HudChartState::new();
// let frame = sample_frame();
//
// state.record_summary(30.0);
// let _ = state.apply_frame(&frame, Some(&[10, 20]));
//
// state.last_frame_seen = Some(Instant::now());
//
// assert!(state.prune_stale().is_none());
// assert_eq!(state.summary_points, vec![30.0]);
// assert_eq!(state.panels.len(), 1);
// }
// }

122
src-tauri/src/serial_core/multi_dim_force.rs
View File

@@ -1,122 +0,0 @@
use ndarray::Array2;
const TOTAL_PRESSURE_LOW_THRESHOLD: usize = 500;
const COP_STABILITY_FRAMES_REQUIRED: usize = 5;
const SENSOR_ROWS: usize = 12;
const SENSOR_COLS: usize = 7;
pub struct PztProcessor {
first_frame: Option<Vec<f32>>,
first_contact_cop_x: Option<f32>,
first_contact_cop_y: Option<f32>,
contact_initialized: bool,
total_pressure_low_counter: usize,
}
impl PztProcessor {
pub fn new() -> Self {
Self {
first_frame: None,
first_contact_cop_x: None,
first_contact_cop_y: None,
contact_initialized: false,
total_pressure_low_counter: 0,
}
}
fn subtract_baseline(&mut self, current_frame: &[f32]) -> Vec<f32> {
if self.first_frame.is_none() {
self.first_frame = Some(current_frame.to_vec());
}
let baseline = self.first_frame.as_ref().unwrap();
current_frame
.iter()
.zip(baseline.iter())
.map(|(c, b)| (c - b).max(0.0))
.collect()
}
fn reset_cop_state(&mut self) {
self.first_contact_cop_x = None;
self.first_contact_cop_y = None;
self.contact_initialized = false;
self.total_pressure_low_counter = 0;
}
fn compute_pressure_direction(&mut self, frame: &[f32]) -> (f32, f32) {
let frame2d = Array2::from_shape_vec((SENSOR_ROWS, SENSOR_COLS), frame.to_vec()).unwrap();
let total_pressure: f32 = frame2d.sum();
if total_pressure < TOTAL_PRESSURE_LOW_THRESHOLD as f32 {
self.total_pressure_low_counter += 1;
} else {
self.total_pressure_low_counter = 0;
}
if self.total_pressure_low_counter >= COP_STABILITY_FRAMES_REQUIRED {
self.reset_cop_state();
return (0.0, 0.0);
}
if total_pressure == 0.0 {
return (0.0, 0.0);
}
let mut sum_x = 0.0;
let mut sum_y = 0.0;
for r in 0..SENSOR_ROWS {
for c in 0..SENSOR_COLS {
let val = frame2d[(r, c)];
sum_x += val * c as f32;
sum_y += val * r as f32;
}
}
let cop_x = sum_x / total_pressure;
let cop_y = sum_y / total_pressure;
if !self.contact_initialized {
self.first_contact_cop_x = Some(cop_x);
self.first_contact_cop_y = Some(cop_y);
self.contact_initialized = true;
return (0.0, 0.0);
}
let dx = cop_x - self.first_contact_cop_x.unwrap();
let dy = cop_y - self.first_contact_cop_y.unwrap();
(dx, dy)
}
fn compute_vector_angle(x: f32, y: f32) -> (f32, f32) {
let epsilon = 1e-8;
let mag = (x * x + y * y).sqrt();
let mut angle = (y).atan2(x + epsilon).to_degrees();
if angle < 0.0 {
angle += 360.0;
}
(angle, mag)
}
fn compute_pzt_angle(px: f32, py: f32) -> (f32, f32) {
Self::compute_vector_angle(px, -py)
}
pub fn get_pzt_angle(&mut self, adc_data: &[f32]) -> Result<f32, &'static str> {
if adc_data.len() != 84 {
return Err("ADC data length must be 84");
}
let baseline = self.subtract_baseline(adc_data);
let (dx, dy) = self.compute_pressure_direction(&baseline);
let (angle, _) = Self::compute_pzt_angle(dx, dy);
Ok(angle)
}
pub fn reset_baseline(&mut self) {
self.first_frame = None;
self.reset_cop_state();
}
}

88
src-tauri/src/serial_core/record.rs
View File

@@ -1,3 +1,5 @@
use eskin_finger_sdk::types::FingerSample;
#[derive(Clone)]
pub struct FrameTiming {
pub pts_ms: Option<u64>,
@@ -7,50 +9,82 @@ pub struct FrameTiming {
#[derive(Clone)]
pub struct RecordedFrame<F> {
pub timing: FrameTiming,
pub frame: F
pub frame: F,
}
#[derive(Clone, Default)]
pub struct Recording<F> {
pub frames: Vec<RecordedFrame<F>>
pub frames: Vec<RecordedFrame<F>>,
}
impl<F> Recording<F> {
pub fn new() -> Recording<F> { Self { frames: Vec::new() } }
pub fn push(&mut self, ite: RecordedFrame<F>) {
self.frames.push(ite);
pub fn new() -> Recording<F> {
Self {
frames: Vec::new(),
}
}
pub fn push(&mut self, item: RecordedFrame<F>) {
self.frames.push(item);
}
}
pub trait CsvExporter<F> {
type Error: std::error::Error + Send + Sync + 'static;
fn csv_header(&self, recording: &Recording<F>) -> Vec<String>;
fn csv_row(&self, item: &RecordedFrame<F>) -> anyhow::Result<Vec<String>>;
}
pub type FingerRecording = Recording<FingerSample>;
// TODO: CsvImporter
pub trait CsvImporter<P> {
fn load<R: std::io::Read>(&mut self, reader: R) -> anyhow::Result<Vec<P>>;
}
pub fn write_csv<F, E, W>(
recording: &Recording<F>,
exporter: &E,
writer: W,
pub fn export_recording_csv<W>(
recording: &Recording<FingerSample>,
mut writer: W,
) -> anyhow::Result<()>
where
E: CsvExporter<F>,
W: std::io::Write,
{
let header = exporter.csv_header(&recording);
let mut wrt = csv::Writer::from_writer(writer);
wrt.write_record(header)?;
for f in &recording.frames {
let row = exporter.csv_row(f)?;
wrt.write_record(&row)?;
// Infer channel count from the first sample's combined_forces (just fz)
// We write: timestamp_us, sequence, module, fx, fy, fz
let mut wrt = csv::Writer::from_writer(&mut writer);
wrt.write_record(["timestamp_us", "sequence", "module", "fx", "fy", "fz"])?;
for frame in &recording.frames {
let s = &frame.frame;
wrt.write_record(&[
s.timestamp_us.to_string(),
s.sequence.to_string(),
format!("{:?}", s.combined_forces.module),
s.combined_forces.force.fx.to_string(),
s.combined_forces.force.fy.to_string(),
s.combined_forces.force.fz.to_string(),
])?;
}
wrt.flush()?;
Ok(())
}
pub struct FingerSampleCsvPacket {
pub timestamp_us: u64,
pub sequence: u32,
pub fz: u32,
}
pub fn import_csv<R: std::io::Read>(
reader: R,
) -> anyhow::Result<Vec<FingerSampleCsvPacket>> {
let mut rdr = csv::Reader::from_reader(reader);
let mut packets = Vec::new();
for result in rdr.records() {
let record = result?;
if record.len() < 6 {
continue;
}
let timestamp_us = record.get(0).unwrap_or("0").parse::<u64>().unwrap_or(0);
let sequence = record.get(1).unwrap_or("0").parse::<u32>().unwrap_or(0);
let fz = record.get(5).unwrap_or("0").parse::<u32>().unwrap_or(0);
packets.push(FingerSampleCsvPacket {
timestamp_us,
sequence,
fz,
});
}
Ok(packets)
}

529
src-tauri/src/serial_core/serial.rs
View File

@@ -1,443 +1,160 @@
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};
use crate::serial_core::model::{HudChartState, HudPacket};
#[cfg(feature = "multi-dim")]
use crate::serial_core::multi_dim_force::PztProcessor;
use crate::serial_core::model::HudChartState;
use crate::serial_core::record::Recording;
use crate::serial_core::record::{FrameTiming, RecordedFrame};
#[cfg(feature = "devkit")]
use crate::devkit::{proto::SensorFrame, DevKitState};
use anyhow::Result;
use log::debug;
use std::future::pending;
#[cfg(feature = "devkit")]
use std::sync::atomic::Ordering;
use std::sync::{Arc, Mutex};
use std::time::Instant;
use eskin_finger_sdk::channel::DeviceEvent;
use eskin_finger_sdk::config::DeviceConfig;
use eskin_finger_sdk::device::{EskinDevice, EskinDeviceInner};
use eskin_finger_sdk::transport::SerialPortTransport;
use eskin_finger_sdk::types::FingerSample;
use tauri::{AppHandle, Emitter};
#[cfg(feature = "devkit")]
use tauri::Manager;
use tokio::io::{AsyncReadExt, AsyncWriteExt};
use tokio::time::{self, Duration, MissedTickBehavior};
use tokio_serial::SerialStream;
use tokio_util::sync::CancellationToken;
const AUTO_SUB_INTERVAL: Duration = Duration::from_nanos(16_666_667);
use super::model::HudPacket;
pub enum PollMode<F> {
Disable,
Enabled(Box<dyn PollRequester<F>>),
pub struct SdkSession {
pub device: EskinDeviceInner,
}
struct PendingSubFrame<F> {
frame: F,
values: Vec<i32>,
pub fn open_device(port: &str) -> Result<SdkSession, String> {
let port = port.trim();
if port.is_empty() {
return Err("Serial port is required".to_string());
}
let transport = SerialPortTransport::new(port, 921600);
let config = DeviceConfig::default();
let mut device = EskinDeviceInner::new(config, Box::new(transport));
device.open().map_err(|e| e.to_string())?;
Ok(SdkSession { device })
}
pub trait SerialFrame: Clone + Send + 'static {
fn dts_ms(&self) -> u64;
fn to_hud_packet(
&self,
chart_state: &mut HudChartState,
display_values: Option<&[i32]>,
) -> Option<HudPacket>;
}
impl SerialFrame for TestFrame {
fn dts_ms(&self) -> u64 {
self.dts_ms
}
fn to_hud_packet(
&self,
chart_state: &mut HudChartState,
display_values: Option<&[i32]>,
) -> Option<HudPacket> {
Some(chart_state.apply_frame(self, display_values))
}
}
impl SerialFrame for TactileAFrame {
fn dts_ms(&self) -> u64 {
match self {
TactileAFrame::Req(_) => 0,
TactileAFrame::Rep(rep) => rep.dts_ms,
}
}
fn to_hud_packet(
&self,
chart_state: &mut HudChartState,
display_values: Option<&[i32]>,
) -> Option<HudPacket> {
match self {
TactileAFrame::Req(_) => None,
TactileAFrame::Rep(rep) => {
let proxy = TestFrame {
header: rep.meta.header,
cmd: rep.meta.func_code,
length: rep.meta.except_data_len,
payload: rep.payload.clone(),
checksum: rep.meta.checksum,
dts_ms: rep.dts_ms,
};
Some(chart_state.apply_frame(&proxy, display_values))
}
}
}
}
pub trait PollRequester<F>: Send {
fn poll_interval(&self) -> Option<Duration> {
None
}
fn should_request(&mut self) -> bool {
true
}
fn next_request(&mut self) -> Result<Option<F>> {
Ok(None)
}
fn on_rx_frame(&mut self, _frame: &F) {}
}
#[derive(Default)]
pub struct NoopPollRequester;
impl<F> PollRequester<F> for NoopPollRequester {}
pub struct TactileAPollRequester {
period: Duration,
cols: usize,
rows: usize,
awaiting_reply: bool,
last_request_at: Option<Instant>,
reply_timeout: Duration,
}
impl TactileAPollRequester {
pub fn new(period: Duration, cols: usize, rows: usize, reply_timeout: Duration) -> Self {
Self {
period,
cols,
rows,
awaiting_reply: false,
last_request_at: None,
reply_timeout,
}
}
}
impl PollRequester<TactileAFrame> for TactileAPollRequester {
fn poll_interval(&self) -> Option<Duration> {
Some(self.period)
}
fn should_request(&mut self) -> bool {
if !self.awaiting_reply {
return true;
}
let timed_out = self
.last_request_at
.map(|t| t.elapsed() >= self.reply_timeout)
.unwrap_or(false);
if timed_out {
self.awaiting_reply = false;
self.last_request_at = None;
return true;
}
false
}
fn next_request(&mut self) -> Result<Option<TactileAFrame>> {
let req = TactileACodec::build_req_frame(self.cols, self.rows)?;
self.awaiting_reply = true;
self.last_request_at = Some(Instant::now());
Ok(Some(req))
}
fn on_rx_frame(&mut self, frame: &TactileAFrame) {
if matches!(frame, TactileAFrame::Rep(_)) {
self.awaiting_reply = false;
self.last_request_at = None
}
}
}
pub async fn run_serial<C, H, T, F>(
pub async fn run_stream(
app: AppHandle,
port: SerialStream,
codec: C,
handler: H,
session_started_at: Instant,
recording: Arc<Mutex<Recording<F>>>,
device: &mut EskinDeviceInner,
cancel: CancellationToken,
) -> Result<()>
where
F: SerialFrame,
C: Codec<F> + Send + 'static,
H: FrameHandler<F, T> + Send + 'static,
T: Into<i32>,
{
run_serial_with_poll(
app,
port,
codec,
handler,
session_started_at,
recording,
cancel,
PollMode::Disable,
)
.await
}
pub async fn run_serial_with_poll<C, H, T, F>(
app: AppHandle,
mut port: SerialStream,
mut codec: C,
mut handler: H,
session_started_at: Instant,
recording: Arc<Mutex<Recording<F>>>,
cancel: CancellationToken,
poll_mode: PollMode<F>,
) -> Result<()>
where
F: SerialFrame,
C: Codec<F> + Send + 'static,
H: FrameHandler<F, T> + Send + 'static,
T: Into<i32>,
{
let mut requester = match poll_mode {
PollMode::Disable => None,
PollMode::Enabled(r) => Some(r),
};
let mut poll_interval = requester.as_ref().and_then(|r| r.poll_interval()).map(|d| {
let mut it = time::interval(d);
it.set_missed_tick_behavior(MissedTickBehavior::Skip);
it
});
let mut poll_sub_interval = time::interval(AUTO_SUB_INTERVAL);
poll_sub_interval.set_missed_tick_behavior(MissedTickBehavior::Skip);
) -> Result<(), String> {
device
.start_stream()
.map_err(|e| format!("start_stream failed: {e}"))?;
let channels = device.channels();
let mut chart_state = HudChartState::new();
let mut buffer = [0u8; 1024];
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);
loop {
let result = loop {
tokio::select! {
_ = cancel.cancelled() => break,
_ = async {
match poll_interval.as_mut() {
Some(it) => {
it.tick().await;
_ = cancel.cancelled() => {
break Ok(());
}
None => pending::<()>().await,
_ = tokio::time::sleep(tokio::time::Duration::from_millis(1)) => {}
}
} => {
if let Some(r) = requester.as_mut() {
if r.should_request() {
if let Some(req) = r.next_request()? {
let bytes = codec.encode(&req)?;
port.write_all(&bytes).await?;
}
}
}
}
_ = prune_interval.tick() => {
if let Some(packet) = chart_state.prune_stale() {
app.emit("hud_stream", packet)?;
}
}
_ = poll_sub_interval.tick() => {
if let Some(pending) = pending_sub_frame.take() {
let display_values = build_display_values(
&mut chart_state,
pending.values.as_slice(),
);
if let Some(packet) = pending
.frame
.to_hud_packet(&mut chart_state, display_values.as_deref())
// Try to receive a sample (non-blocking-ish via small timeout)
match channels.recv_sample(5) {
Ok(sample) => {
if let Some(packet) = build_hud_packet_from_sample(&sample, &mut chart_state) {
let _ = app.emit("hud_stream", packet);
}
}
Err(eskin_finger_sdk::error::SdkError::Timeout) => {
// No sample yet, check for events
}
Err(e) => {
break Err(format!("sample recv error: {e}"));
}
}
// Drain any events
if let Err(e) = drain_events(&channels) {
break Err(e);
}
};
let _ = device.stop_stream();
result
}
pub async fn run_stream_with_record(
app: AppHandle,
device: &mut EskinDeviceInner,
cancel: CancellationToken,
recording: std::sync::Arc<std::sync::Mutex<Recording<FingerSample>>>,
) -> Result<(), String> {
device
.start_stream()
.map_err(|e| format!("start_stream failed: {e}"))?;
let channels = device.channels();
let mut chart_state = HudChartState::new();
let result = loop {
tokio::select! {
_ = cancel.cancelled() => {
break Ok(());
}
_ = tokio::time::sleep(tokio::time::Duration::from_millis(1)) => {}
}
match channels.recv_sample(5) {
Ok(sample) => {
// Record
{
app.emit("hud_stream", packet)?;
}
}
}
read_result = port.read(&mut buffer) => {
let n = read_result?;
if n == 0 {
// Some serial drivers can resolve reads with 0 bytes repeatedly.
// Yield here so timer-driven poll requests are not starved by a busy loop.
tokio::task::yield_now().await;
continue;
}
let frames = codec.decode(&buffer[..n], session_started_at)?;
for frame in frames {
if let Some(r) = requester.as_mut() {
r.on_rx_frame(&frame);
}
let decode_res = handler
.on_frame(&frame)
.await?
.map(|vals| vals.into_iter().map(Into::into).collect::<Vec<i32>>());
let mut record = recording
.lock()
.map_err(|_| anyhow::anyhow!("recording state poisoned"))?;
record.push(RecordedFrame {
timing: FrameTiming {
.map_err(|_| "recording state poisoned".to_string())?;
record.push(crate::serial_core::record::RecordedFrame {
timing: crate::serial_core::record::FrameTiming {
pts_ms: None,
dts_ms: frame.dts_ms(),
dts_ms: sample.timestamp_us / 1000,
},
frame: frame.clone(),
frame: sample.clone(),
});
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>>();
if let Ok(angle) = pzt_processor.get_pzt_angle(&pzt_values) {
// debug!("pzt angle: {:.2}", angle);
if let Some(packet) = build_hud_packet_from_sample(&sample, &mut chart_state) {
let _ = app.emit("hud_stream", packet);
}
}
#[cfg(feature = "devkit")]
{
let summary = vals.iter().copied().sum::<i32>();
let force = raw_to_g1(summary as u32);
push_devkit_frame(&app, vals.as_slice(), frame.dts_ms(), force);
Err(eskin_finger_sdk::error::SdkError::Timeout) => {}
Err(e) => {
break Err(format!("sample recv error: {e}"));
}
}
pending_sub_frame = Some(PendingSubFrame {
frame: frame.clone(),
values: vals,
});
} else if let Some(packet) = frame.to_hud_packet(&mut chart_state, None) {
app.emit("hud_stream", packet)?;
if let Err(e) = drain_events(&channels) {
break Err(e);
}
}
}
}
}
Ok(())
};
let _ = device.stop_stream();
result
}
fn build_display_values(chart_state: &mut HudChartState, values: &[i32]) -> Option<Vec<i32>> {
let summary = values.iter().copied().sum::<i32>();
let force = raw_to_g1(summary as u32);
chart_state.record_summary(force as f32);
chart_state.record_pressure_matrix(values);
Some(vec![summary])
fn drain_events(channels: &std::sync::Arc<eskin_finger_sdk::channel::ChannelManager>) -> Result<(), String> {
loop {
match channels.recv_event(0) {
Ok(DeviceEvent::IoError(msg)) => {
eprintln!("SDK stream io error: {msg}");
return Err(format!("stream io error: {msg}"));
}
Ok(_) => {}
Err(eskin_finger_sdk::error::SdkError::Timeout) => return Ok(()),
Err(eskin_finger_sdk::error::SdkError::ChannelClosed) => {
return Err("event channel closed".into());
}
Err(_) => return Ok(()),
}
}
}
#[cfg(feature = "devkit")]
fn push_devkit_frame(app: &AppHandle, values: &[i32], dts_ms: u64, resultant_force: f64) {
let devkit_state = app.state::<DevKitState>();
if !devkit_state.running.load(Ordering::Relaxed) {
return;
fn build_hud_packet_from_sample(
sample: &FingerSample,
chart_state: &mut HudChartState,
) -> Option<HudPacket> {
let fz = sample.combined_forces.force.fz as f32;
chart_state.record_summary(fz);
if !sample.raw_adcs.is_empty() {
let pressure: Vec<f32> = sample.raw_adcs.iter().map(|&v| v as f32).collect();
chart_state.record_pressure_matrix(&pressure);
}
let (rows, cols) = infer_matrix_shape(values.len());
let timestamp_ms = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_millis() as u64;
let seq = timestamp_ms;
let matrix = values
.iter()
.map(|value| (*value).max(0) as u32)
.collect::<Vec<_>>();
devkit_state.push_frame(SensorFrame {
seq,
timestamp_ms,
rows,
cols,
matrix,
resultant_force,
dts_ms: dts_ms as u32,
});
}
#[cfg(feature = "devkit")]
fn infer_matrix_shape(len: usize) -> (u32, u32) {
if len == 84 {
return (12, 7);
}
if len == 0 {
return (0, 0);
}
let mut best = (len, 1);
let mut factor = 1usize;
while factor * factor <= len {
if len % factor == 0 {
best = (len / factor, factor);
}
factor += 1;
}
(best.0 as u32, best.1 as u32)
}
fn raw_to_g1(raw: u32) -> f64 {
const X: [u32; 12] = [
0, 84402, 117218, 140176, 159126, 175812, 191484, 208758, 224703, 252448, 302361, 352703,
];
const Y: [f64; 12] = [
0.0, 160.0, 260.0, 360.0, 460.0, 560.0, 660.0, 760.0, 860.0, 1060.0, 1560.0, 2060.0,
];
let n = X.len();
if raw <= X[0] {
return Y[0] / 100.0;
}
if raw >= X[n - 1] {
return Y[n - 1] / 100.0;
}
let mut left = 0;
let mut right = n - 1;
while left + 1 < right {
let mid = (left + right) / 2;
if raw < X[mid] {
right = mid;
} else {
left = mid;
}
}
let ratio = (raw - X[left]) as f64 / (X[right] - X[left]) as f64;
Y[left] / 100.0 + ratio * (Y[right] - Y[left]) / 100.0
Some(chart_state.build_snapshot())
}

59
src-tauri/src/serial_core/utils.rs
View File

@@ -1,59 +0,0 @@
use std::time::Instant;
pub fn usize_to_u16_be_bytes(n: usize) -> [u8; 2] {
(n as u16).to_be_bytes()
}
pub fn usize_to_u16_le_bytes(n: usize) -> [u8; 2] {
(n as u16).to_be_bytes()
}
pub fn u16_to_hex_be_bytes(n: u16) -> [u8; 2] {
(n as u16).to_be_bytes()
}
pub fn u16_to_hex_le_bytes(n: u16) -> [u8; 2] {
(n as u16).to_le_bytes()
}
pub fn calc_crc8_smbus(c: &[u8]) -> u8 {
let crc8_smbus = crc::Crc::<u8>::new(&crc::CRC_8_SMBUS);
let checksum = crc8_smbus.checksum(c);
return checksum;
}
pub fn calc_crc8_itu(c: &[u8]) -> u8 {
let crc8_itu_alg = crc::Crc::<u8>::new(&crc::CRC_8_I_432_1);
let checksum = crc8_itu_alg.checksum(c);
return checksum;
}
pub fn elapsed_millis(start_at: Instant) -> u64 {
start_at.elapsed().as_millis() as u64
}
#[cfg(test)]
mod test {
use anyhow::Ok;
use crate::serial_core::utils::{calc_crc8_itu, calc_crc8_smbus};
#[test]
fn test_crc8_itu() -> anyhow::Result<()> {
let req_vec = vec![0x55, 0xAA, 0x09, 0x00, 0x34, 0x00, 0xFB, 0x00, 0x1C, 0x00, 0x00, 0x18, 0x00];
let checksum = calc_crc8_itu(req_vec.as_slice());
assert_eq!(checksum, 0x7A);
Ok(())
}
#[test]
fn test_crc8_smbus() -> anyhow::Result<()> {
let req_vec = vec![0x55, 0xAA, 0x09, 0x00, 0x34, 0x00, 0xFB, 0x00, 0x1C, 0x00, 0x00, 0x18, 0x00];
let checksum = calc_crc8_smbus(req_vec.as_slice());
assert_eq!(checksum, 0x2F);
Ok(())
}
}

2
src-tauri/tauri.conf.json
View File

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

747
src/indicator/indicator498.py
View File

@@ -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