新增统一力读数会话 ForceSession

把同帧测量、空载归零、模型推断与共用后处理收进一个 SDK 会话，Studio 显示与记录使用同一样本；旧 Sensor 读数接口保持兼容。以 force session 与受影响 SDK 套件、lint 与文档门禁验证，硬件与真实载荷精度未验证。

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CpCfJF6SvonMKmPZhzKZHb

tests/test_signal_processing.py

@@ -14,6 +14,7 @@ from orisys.signal_processing import (
     NORMAL_FORCE_CAL_OFFSET,
     NORMAL_FORCE_CAL_SLOPE,
     LowpassFilter,
+    MultiAxisCausalFilter,
     calibrate_normal_force,
     calibrate_shear_components,
     calibrate_shear_force,
@@ -494,3 +495,76 @@ def test_the_calibration_note_names_the_bundle_that_was_found(tmp_path, monkeypa
     # A dict always keys ``found_root``, so guard its value: an unguarded copy of
     # this branch prints "bundle found at None".
     assert "None" not in note
+
+
+# --------------------------------------------- multi-axis causal force chain
+class _ReferenceMedian:
+    """Independent median window, so the operator test has its own expectation."""
+
+    def __init__(self, window):
+        self.window = window
+        self.history = []
+
+    def process(self, value):
+        self.history.append(float(value))
+        self.history = self.history[-self.window :]
+        return float(np.median(self.history))
+
+
+def test_multi_axis_median_matches_a_plain_window_per_axis():
+    chain = MultiAxisCausalFilter(median_window=3)
+    reference = [_ReferenceMedian(3) for _ in range(3)]
+    samples = [(10.0, 20.0, 30.0), (90.0, 20.0, 30.0), (10.0, 20.0, 30.0),
+               (10.0, 20.0, 30.0)]
+    for sample in samples:
+        actual = chain.process(sample)
+        expected = [reference[index].process(value) for index, value in enumerate(sample)]
+        np.testing.assert_allclose(actual, expected)
+
+
+def test_multi_axis_nan_axis_does_not_enter_history_or_pull_the_others():
+    chain = MultiAxisCausalFilter(median_window=3)
+    chain.process((10.0, 20.0, 30.0))
+    skipped = chain.process(
+        (float("nan"), 20.0, 30.0), valid=(False, True, True)
+    )
+    assert np.isnan(skipped[0])
+    assert skipped[1] == pytest.approx(20.0)
+    # Valid axes keep advancing while the missing axis is skipped.
+    resumed = chain.process((10.0, 20.0, 30.0))
+    assert resumed[0] == pytest.approx(10.0)
+    # A NaN arriving without an explicit validity flag is treated as unavailable.
+    assert np.isnan(chain.process((float("nan"), 20.0, 30.0))[0])
+
+
+def test_multi_axis_all_stages_off_is_exact_passthrough_including_nan():
+    chain = MultiAxisCausalFilter(median_window=1, lowpass_cutoff_hz=0.0)
+    assert chain.enabled is False
+    sample = (1.5, -2.0, float("nan"))
+    first = chain.process(sample)
+    second = chain.process(sample)
+    assert first[0] == 1.5 and first[1] == -2.0 and np.isnan(first[2])
+    assert second[0] == 1.5 and second[1] == -2.0 and np.isnan(second[2])
+
+
+def test_multi_axis_lowpass_ignores_missing_axes_and_resets():
+    chain = MultiAxisCausalFilter(
+        median_window=1, lowpass_cutoff_hz=5.0, sample_rate_hz=30.0, n_channels=3
+    )
+    values = []
+    for index in range(20):
+        values.append(
+            chain.process(
+                (float(index), float(index), float(index)),
+                valid=(True, True, index % 2 == 0),
+            )
+        )
+    for index, value in enumerate(values):
+        assert np.isfinite(value[0])
+        assert np.isfinite(value[1])
+        # The third axis is only fed on even frames, so it is missing on odd ones.
+        assert np.isnan(value[2]) == bool(index % 2)
+    chain.reset()
+    after = chain.process((1.0, 2.0, 3.0))
+    # A reset IIR starts from its first sample's steady state.
+    np.testing.assert_allclose(after, (1.0, 2.0, 3.0))