Datasets:
Clarify v1.1.1 kinematics and refresh Doppler audit
Browse filesClarify payload/release version semantics, centered non-causal endpoint velocity diagnostics, 0.5 m/s flag scope, and background-actor boundary. Refresh the public array-center Doppler receipt/script with selected-versus-eligible counts, exclusion reasons, and magnitude-threshold stability. All 300 sample TAR archives remain byte-identical.
- README.md +12 -5
- checksums.sha256 +7 -7
- dataset_info.json +1 -1
- metadata/json_schema.json +26 -1
- metadata/kinematics/README.md +16 -7
- metadata/kinematics/patch_manifest.json +4 -2
- metadata/paper_reproducibility/receipts/array_doppler_audit.json +1897 -159
- metadata/paper_reproducibility/scripts/analyze_scene2wave_array_doppler.py +218 -24
README.md
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@@ -41,15 +41,22 @@ generation toolchain or editable simulator assets are included.
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## v1.1.1 metadata compatibility patch
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Version
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`metadata/kinematics/pose_velocity_v1_1_1.npz`. The original velocity is
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exposed as `tx_route_command_velocity_mps` and
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`rx_route_command_velocity_mps`; the corresponding
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`tx_pose_derived_velocity_mps` and `rx_pose_derived_velocity_mps` arrays are
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The mismatch was caused by the older waypoint-motion writer using a route
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orientation for velocity while position followed the interpolation-segment
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## v1.1.1 metadata compatibility patch
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**Version convention:** v1.1.0 denotes the immutable 300-episode data payload;
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v1.1.1 denotes the current public release, comprising those byte-identical TAR
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archives plus additive kinematics and paper-audit metadata. Version 1.1.1
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adds an explicit kinematics sidecar at
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`metadata/kinematics/pose_velocity_v1_1_1.npz`. The original velocity is
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exposed as `tx_route_command_velocity_mps` and
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`rx_route_command_velocity_mps`; the corresponding
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`tx_pose_derived_velocity_mps` and `rx_pose_derived_velocity_mps` arrays are
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+
centered finite differences over a total 10-ms span for interior states, with
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documented one-sided episode boundaries. They are offline geometric
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diagnostics, not temporally causal forecasting inputs; causal tasks should
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derive backward differences from positions available at or before the anchor.
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Per-frame closure errors and documented 0.5-m/s diagnostic consistency flags
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are included. A false flag warns against treating the legacy route-command
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velocity as a pose derivative; it does not invalidate pose, sensing, or RF
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data.
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The mismatch was caused by the older waypoint-motion writer using a route
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orientation for velocity while position followed the interpolation-segment
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checksums.sha256
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@@ -3,7 +3,7 @@ dd40946c2c96108d18ff048be3295735dd29ea34e8e1b18301c9cf30ec8f5775 ATTRIBUTION.md
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aa345cc056da5ea8bc5d6a856a1329a249395fc8e046a1fa5136a5e08c1b2ebd DATA_PROVENANCE.md
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f3972567e1b25c4e0c746c5344a1165b7e8fdcb51690142a70a95aa8b60b0ca3 LICENSE
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b327c5c7fa9ed801d4c460ba2e56fd2bd25d2208b96db1657be49a42fe122008 LICENSES/CC-BY-4.0.txt
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-
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9ea776ffa9f96d7e21af140a7df1f3fef9e19512cc841b7b4e8e7428de3d571b THIRD_PARTY_NOTICES.md
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a2c19f34ade8a2dc45f9c15d92c3db282c7636db7a0fb473371407cbf49da4d3 data/main_training/Town01/10kmh/core/Req_Town01_dynamic_10kmh_t001_rfmain.tar
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c73d7994d672928c5e56b7bedf0e9ab28ec4743bdc91c5db99912641a1e52e22 data/main_training/Town01/10kmh/core/Req_Town01_dynamic_10kmh_t002_rfmain.tar
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@@ -305,21 +305,21 @@ a842189a601cddd12e0c402ca86d104b4e014b7eadec6b201d1ee1dfacec29dc data/main_trai
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0bda3ee88933ff021de914822791fa7f7f86ff8cce127a109f0392bc8e8fa8f3 data/main_training/Town15/static/multipath/Req_Town15_static_p000_rfmain.tar
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efea75de0b6439eb2a7855dd39d8072d32afaf658eadffc3babb04de44396cc2 data/main_training/Town15/static/scattering_medium/Req_Town15_static_p006_rfsupp01.tar
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01bfa06f9420da4356a4e23657cd1ed1a2cba07af7f38818573306174a3a6816 data/main_training/Town15/static/scattering_mild/Req_Town15_static_p011_rfcarlasafe01.tar
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-
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310edd6c6c41f7254de9b2926fb2b4c2dcc1ecc6a42e837eee94fe24dd172257 metadata/generation_batches.json
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26d381cb9b9693f46c7e69af974d0c5389210cb54f65502a581be8978c6fd02c metadata/generation_config.yaml
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094e2d9bf5c7f3ac53d1b7399adf2b0e792034786076cd8d9ef121ca9524d594 metadata/generation_configs/extension_8town.yaml
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e973af8415e2a831c32a6379a64dfb34221bf1b7773754d5196ff822d7d1b0d8 metadata/generation_configs/original_4town.yaml
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-
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50b40b3a7e3880f31fdccb573273aa14669bb55baadba04cbfa228226aa7c787 metadata/kinematics/pose_velocity_v1_1_1.npz
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d87c7f6b7dd7e9f0c83b000ce8dfdf55ce37f2f262457cbead6c3062388e75af metadata/kinematics/schema.json
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-
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a04fca352a363bee54b77ced5ae4b5e9f3e88909a20976e1b99ca52a3bea618b metadata/paper_reproducibility/receipts/data_contract_audit.json
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c0cc5444a84a7c2d2e6ee381c7e0eae73d990e6240daac9d8b869b98f370dfbd metadata/paper_reproducibility/receipts/generation_batch_audit.json
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d35f773fbdbc27c8c73888c7b0747396261a860745be5cb17a1fbb9d16f4b2ca metadata/paper_reproducibility/receipts/material_evidence_audit.json
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-
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ab28529dbe1cc4e33659306f81650c57d4cfc94ff5ab55fd465661bfcced9571 metadata/paper_reproducibility/scripts/analyze_scene2wave_data_contracts.py
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dddea4cdb2265a723b6ed907fbf9a2cd270515ead42c607fa39bca6829bd195a metadata/paper_reproducibility/scripts/analyze_scene2wave_generation_batches.py
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56b556d8e53e7816ba2f52fb5148156d7c6bcdf207c58d2d59e29b70a127363b metadata/paper_reproducibility/scripts/analyze_scene2wave_material_evidence.py
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aa345cc056da5ea8bc5d6a856a1329a249395fc8e046a1fa5136a5e08c1b2ebd DATA_PROVENANCE.md
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f3972567e1b25c4e0c746c5344a1165b7e8fdcb51690142a70a95aa8b60b0ca3 LICENSE
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b327c5c7fa9ed801d4c460ba2e56fd2bd25d2208b96db1657be49a42fe122008 LICENSES/CC-BY-4.0.txt
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+
402cd533b62c766e49be09fe3e2848856c5c543be5cc05da90423724fbeb3527 README.md
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9ea776ffa9f96d7e21af140a7df1f3fef9e19512cc841b7b4e8e7428de3d571b THIRD_PARTY_NOTICES.md
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a2c19f34ade8a2dc45f9c15d92c3db282c7636db7a0fb473371407cbf49da4d3 data/main_training/Town01/10kmh/core/Req_Town01_dynamic_10kmh_t001_rfmain.tar
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c73d7994d672928c5e56b7bedf0e9ab28ec4743bdc91c5db99912641a1e52e22 data/main_training/Town01/10kmh/core/Req_Town01_dynamic_10kmh_t002_rfmain.tar
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0bda3ee88933ff021de914822791fa7f7f86ff8cce127a109f0392bc8e8fa8f3 data/main_training/Town15/static/multipath/Req_Town15_static_p000_rfmain.tar
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efea75de0b6439eb2a7855dd39d8072d32afaf658eadffc3babb04de44396cc2 data/main_training/Town15/static/scattering_medium/Req_Town15_static_p006_rfsupp01.tar
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01bfa06f9420da4356a4e23657cd1ed1a2cba07af7f38818573306174a3a6816 data/main_training/Town15/static/scattering_mild/Req_Town15_static_p011_rfcarlasafe01.tar
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5f6acfb9d7d23cd712571e2732d1fd9adee33490136d99de50dbeb03b559c87e dataset_info.json
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310edd6c6c41f7254de9b2926fb2b4c2dcc1ecc6a42e837eee94fe24dd172257 metadata/generation_batches.json
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26d381cb9b9693f46c7e69af974d0c5389210cb54f65502a581be8978c6fd02c metadata/generation_config.yaml
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094e2d9bf5c7f3ac53d1b7399adf2b0e792034786076cd8d9ef121ca9524d594 metadata/generation_configs/extension_8town.yaml
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e973af8415e2a831c32a6379a64dfb34221bf1b7773754d5196ff822d7d1b0d8 metadata/generation_configs/original_4town.yaml
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e12caa8463e5e71868f7aa432730bdc0ce4c64ff898d567aeb90255645dafe76 metadata/json_schema.json
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482241cf49a28e1013834fde3ae17c240839aa57685d718c3367632d45cba852 metadata/kinematics/README.md
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65f3563de605e08ef165862da601345435cb04850bd04d32c49ae8101bb9c1aa metadata/kinematics/patch_manifest.json
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50b40b3a7e3880f31fdccb573273aa14669bb55baadba04cbfa228226aa7c787 metadata/kinematics/pose_velocity_v1_1_1.npz
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d87c7f6b7dd7e9f0c83b000ce8dfdf55ce37f2f262457cbead6c3062388e75af metadata/kinematics/schema.json
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e4838d44dcc4d220a8e821c18aab0c8757158f2bada188c18bf4557092b0a212 metadata/paper_reproducibility/receipts/array_doppler_audit.json
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a04fca352a363bee54b77ced5ae4b5e9f3e88909a20976e1b99ca52a3bea618b metadata/paper_reproducibility/receipts/data_contract_audit.json
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c0cc5444a84a7c2d2e6ee381c7e0eae73d990e6240daac9d8b869b98f370dfbd metadata/paper_reproducibility/receipts/generation_batch_audit.json
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d35f773fbdbc27c8c73888c7b0747396261a860745be5cb17a1fbb9d16f4b2ca metadata/paper_reproducibility/receipts/material_evidence_audit.json
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+
8100e55117986b2510ed3ba79508ce1419e1be717ee18a90ed9ed5a06b8a60d0 metadata/paper_reproducibility/scripts/analyze_scene2wave_array_doppler.py
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ab28529dbe1cc4e33659306f81650c57d4cfc94ff5ab55fd465661bfcced9571 metadata/paper_reproducibility/scripts/analyze_scene2wave_data_contracts.py
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dddea4cdb2265a723b6ed907fbf9a2cd270515ead42c607fa39bca6829bd195a metadata/paper_reproducibility/scripts/analyze_scene2wave_generation_batches.py
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56b556d8e53e7816ba2f52fb5148156d7c6bcdf207c58d2d59e29b70a127363b metadata/paper_reproducibility/scripts/analyze_scene2wave_material_evidence.py
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dataset_info.json
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@@ -1,5 +1,5 @@
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{
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-
"created_at_utc": "2026-08-
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"data_file_count": 2520600,
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"data_size_bytes": 230903029002,
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"dataset_name": "scene2wave_dataset",
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{
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"created_at_utc": "2026-08-24T07:40:32.924965+00:00",
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"data_file_count": 2520600,
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"data_size_bytes": 230903029002,
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"dataset_name": "scene2wave_dataset",
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metadata/json_schema.json
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"type": "array",
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"uniqueItems": true
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},
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"packaging": {
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"type": "object"
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},
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@@ -202,7 +226,8 @@
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"distributions",
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"included_modalities",
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"excluded",
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"self_contained"
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],
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"type": "object"
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},
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"type": "array",
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"uniqueItems": true
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},
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"metadata_patch": {
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"additionalProperties": false,
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"properties": {
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"archive_payload_changed": {
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"const": false
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},
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"manifest": {
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"const": "metadata/kinematics/patch_manifest.json"
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},
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"sidecar": {
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"const": "metadata/kinematics/pose_velocity_v1_1_1.npz"
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},
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"source_dataset_version": {
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"const": "1.1.0"
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}
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},
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"required": [
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"source_dataset_version",
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"manifest",
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"sidecar",
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"archive_payload_changed"
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],
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"type": "object"
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},
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"packaging": {
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"type": "object"
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},
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"distributions",
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"included_modalities",
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"excluded",
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"self_contained",
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"metadata_patch"
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],
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"type": "object"
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},
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metadata/kinematics/README.md
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@@ -20,10 +20,19 @@ with np.load(root / row["kinematics_sidecar_path"], allow_pickle=False) as data:
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```
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`tx` is the RSU and `rx` is the CAV. Interior pose derivatives use a centered
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-
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the
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```
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`tx` is the RSU and `rx` is the CAV. Interior pose derivatives use a centered
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finite difference over a total 10-ms span (approximately `t-5 ms` to
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`t+5 ms`). The first ten states reuse the forward difference between the
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episode start and 10 ms; the last ten reuse the backward difference between
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the final state and 10 ms before it. These arrays are offline geometric
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diagnostics and are not temporally causal forecasting inputs. A causal task
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must instead derive a backward difference from positions available at or
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before its prediction anchor.
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+
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The 0.5-m/s validity flag is a diagnostic semantic-consistency threshold, not
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a claim of real vehicle-dynamics accuracy and not a validity decision for the
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pose, sensing payload, or RF channel. A false value only warns that the legacy
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route-command velocity should not be interpreted as a pose derivative. Retain
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`tx_route_command_velocity_mps` or `rx_route_command_velocity_mps` only when
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the historical prescribed route-state semantics are intended. The sidecar
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qualifies Tx/Rx endpoints only; background-actor pose remains authoritative
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for RF geometry, and background speed metadata is outside this patch.
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metadata/kinematics/patch_manifest.json
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{
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"archive_policy": "All 300 v1.1.0 sample tar archives are preserved byte-for-byte.",
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-
"created_at_utc": "2026-08-
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"dataset_version": "1.1.1",
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"endpoint_roles": {
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"rx": "CAV",
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}
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},
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"velocity_semantics": {
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"pose_derived_velocity_mps": "Finite difference of released endpoint positions using a centered 10-ms span and one-sided boundary spans.",
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"route_command_velocity_mps": "Original prescribed route-state velocity retained for compatibility.",
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"velocity_closure_error_mps": "Euclidean norm of pose-derived minus route-command velocity.",
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-
"velocity_validity_flag": "Closure error <= 0.5 m/s; a diagnostic
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}
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}
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{
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"archive_policy": "All 300 v1.1.0 sample tar archives are preserved byte-for-byte.",
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+
"created_at_utc": "2026-08-24T07:40:32.924965+00:00",
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"dataset_version": "1.1.1",
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"endpoint_roles": {
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"rx": "CAV",
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}
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},
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"velocity_semantics": {
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"pose_derived_velocity_causality": "Offline diagnostic: centered interior values use future positions and are not causal forecasting inputs; causal tasks must derive backward differences from position history.",
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"pose_derived_velocity_mps": "Finite difference of released endpoint positions using a centered 10-ms span and one-sided boundary spans.",
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"route_command_velocity_mps": "Original prescribed route-state velocity retained for compatibility.",
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+
"scope": "Tx/Rx endpoints only; background-actor pose is authoritative for RF geometry and background speed metadata is outside this patch.",
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"velocity_closure_error_mps": "Euclidean norm of pose-derived minus route-command velocity.",
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+
"velocity_validity_flag": "Closure error <= 0.5 m/s; a semantic-consistency diagnostic for the legacy route-command field, not a validity decision for pose, sensing, or RF data."
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}
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}
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metadata/paper_reproducibility/receipts/array_doppler_audit.json
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@@ -12,7 +12,51 @@
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7.75
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]
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},
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| 15 |
"center_equivalent_vs_position_increment": {
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| 16 |
"episode_bootstrap_linear_fit_95ci": {
|
| 17 |
"intercept_hz": [
|
| 18 |
-8.350328281484139e-05,
|
|
@@ -26,7 +70,7 @@
|
|
| 26 |
1.000000014804258
|
| 27 |
]
|
| 28 |
},
|
| 29 |
-
"episode_count":
|
| 30 |
"episode_mean_bias_hz": -2.1684125403236113e-05,
|
| 31 |
"episode_mean_linear_fit": {
|
| 32 |
"episode_count": 14,
|
|
@@ -36,12 +80,15 @@
|
|
| 36 |
"episode_mean_mae_hz": 0.10046421192500336,
|
| 37 |
"episode_mean_max_abs_hz": 0.4361745479695031,
|
| 38 |
"episode_mean_median_abs_hz": 0.08363949805165914,
|
| 39 |
-
"episode_mean_p95_abs_hz": 0.
|
| 40 |
"episode_mean_pearson_r": 0.9999999999998328,
|
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|
| 41 |
"pair_count": 24386,
|
| 42 |
-
"pooled_max_abs_hz": 0.692259747610251
|
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|
| 43 |
},
|
| 44 |
"center_equivalent_vs_stored_velocity": {
|
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|
| 45 |
"episode_bootstrap_linear_fit_95ci": {
|
| 46 |
"intercept_hz": [
|
| 47 |
-0.25610807983767153,
|
|
@@ -55,58 +102,294 @@
|
|
| 55 |
1.0087411799668777
|
| 56 |
]
|
| 57 |
},
|
| 58 |
-
"episode_count":
|
| 59 |
"episode_mean_bias_hz": 0.44569819283990736,
|
| 60 |
"episode_mean_linear_fit": {
|
| 61 |
"episode_count": 14,
|
| 62 |
"intercept_hz": 0.49202777151407323,
|
| 63 |
"slope": 1.0022301919972967
|
| 64 |
},
|
| 65 |
-
"episode_mean_mae_hz": 2.
|
| 66 |
"episode_mean_max_abs_hz": 12.47455763247849,
|
| 67 |
"episode_mean_median_abs_hz": 1.4070025822853423,
|
| 68 |
"episode_mean_p95_abs_hz": 9.116030959632598,
|
| 69 |
"episode_mean_pearson_r": 0.9999516429280418,
|
|
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|
| 70 |
"pair_count": 24386,
|
| 71 |
-
"pooled_max_abs_hz": 54.458316091199194
|
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| 72 |
},
|
| 73 |
"episodes": [
|
| 74 |
{
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| 75 |
"direct_multiplicity_max": 0,
|
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|
| 76 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 77 |
-
"bias_hz":
|
| 78 |
-
"mae_hz":
|
| 79 |
-
"max_abs_hz":
|
| 80 |
-
"median_abs_hz":
|
| 81 |
-
"p95_abs_hz":
|
| 82 |
"pair_count": 0
|
| 83 |
},
|
| 84 |
"profile": "core",
|
| 85 |
"sample_id": "Req_Town01_dynamic_60kmh_t004_rfmain",
|
| 86 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 87 |
-
"bias_hz":
|
| 88 |
"expected_mean_hz": NaN,
|
| 89 |
-
"mae_hz":
|
| 90 |
-
"max_abs_hz":
|
| 91 |
-
"median_abs_hz":
|
| 92 |
"observed_mean_hz": NaN,
|
| 93 |
-
"p95_abs_hz":
|
| 94 |
"pair_count": 0
|
| 95 |
},
|
| 96 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 97 |
-
"bias_hz":
|
| 98 |
"expected_mean_hz": NaN,
|
| 99 |
-
"mae_hz":
|
| 100 |
-
"max_abs_hz":
|
| 101 |
-
"median_abs_hz":
|
| 102 |
"observed_mean_hz": NaN,
|
| 103 |
-
"p95_abs_hz":
|
| 104 |
"pair_count": 0
|
| 105 |
},
|
| 106 |
"town": "Town01"
|
| 107 |
},
|
| 108 |
{
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| 109 |
"direct_multiplicity_max": 1,
|
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|
| 110 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 111 |
"bias_hz": 2.1780301298352627,
|
| 112 |
"mae_hz": 4.568577960815057,
|
|
@@ -140,177 +423,573 @@
|
|
| 140 |
"town": "Town01"
|
| 141 |
},
|
| 142 |
{
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|
| 143 |
"direct_multiplicity_max": 0,
|
|
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|
|
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|
|
|
| 144 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 145 |
-
"bias_hz":
|
| 146 |
-
"mae_hz":
|
| 147 |
-
"max_abs_hz":
|
| 148 |
-
"median_abs_hz":
|
| 149 |
-
"p95_abs_hz":
|
| 150 |
"pair_count": 0
|
| 151 |
},
|
| 152 |
"profile": "core",
|
| 153 |
"sample_id": "Req_Town02_dynamic_60kmh_t001_rfmain",
|
| 154 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 155 |
-
"bias_hz":
|
| 156 |
"expected_mean_hz": NaN,
|
| 157 |
-
"mae_hz":
|
| 158 |
-
"max_abs_hz":
|
| 159 |
-
"median_abs_hz":
|
| 160 |
"observed_mean_hz": NaN,
|
| 161 |
-
"p95_abs_hz":
|
| 162 |
"pair_count": 0
|
| 163 |
},
|
| 164 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 165 |
-
"bias_hz":
|
| 166 |
"expected_mean_hz": NaN,
|
| 167 |
-
"mae_hz":
|
| 168 |
-
"max_abs_hz":
|
| 169 |
-
"median_abs_hz":
|
| 170 |
"observed_mean_hz": NaN,
|
| 171 |
-
"p95_abs_hz":
|
| 172 |
"pair_count": 0
|
| 173 |
},
|
| 174 |
"town": "Town02"
|
| 175 |
},
|
| 176 |
{
|
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|
| 177 |
"direct_multiplicity_max": 0,
|
|
|
|
|
|
|
|
|
|
| 178 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 179 |
-
"bias_hz":
|
| 180 |
-
"mae_hz":
|
| 181 |
-
"max_abs_hz":
|
| 182 |
-
"median_abs_hz":
|
| 183 |
-
"p95_abs_hz":
|
| 184 |
"pair_count": 0
|
| 185 |
},
|
| 186 |
"profile": "scattering_mild",
|
| 187 |
"sample_id": "Req_Town02_dynamic_60kmh_t018_rfsupp02",
|
| 188 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 189 |
-
"bias_hz":
|
| 190 |
"expected_mean_hz": NaN,
|
| 191 |
-
"mae_hz":
|
| 192 |
-
"max_abs_hz":
|
| 193 |
-
"median_abs_hz":
|
| 194 |
"observed_mean_hz": NaN,
|
| 195 |
-
"p95_abs_hz":
|
| 196 |
"pair_count": 0
|
| 197 |
},
|
| 198 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 199 |
-
"bias_hz":
|
| 200 |
"expected_mean_hz": NaN,
|
| 201 |
-
"mae_hz":
|
| 202 |
-
"max_abs_hz":
|
| 203 |
-
"median_abs_hz":
|
| 204 |
"observed_mean_hz": NaN,
|
| 205 |
-
"p95_abs_hz":
|
| 206 |
"pair_count": 0
|
| 207 |
},
|
| 208 |
"town": "Town02"
|
| 209 |
},
|
| 210 |
{
|
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|
| 211 |
"direct_multiplicity_max": 0,
|
|
|
|
|
|
|
|
|
|
| 212 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 213 |
-
"bias_hz":
|
| 214 |
-
"mae_hz":
|
| 215 |
-
"max_abs_hz":
|
| 216 |
-
"median_abs_hz":
|
| 217 |
-
"p95_abs_hz":
|
| 218 |
"pair_count": 0
|
| 219 |
},
|
| 220 |
"profile": "scattering_medium",
|
| 221 |
"sample_id": "Req_Town03_dynamic_60kmh_t004_rfmain",
|
| 222 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 223 |
-
"bias_hz":
|
| 224 |
"expected_mean_hz": NaN,
|
| 225 |
-
"mae_hz":
|
| 226 |
-
"max_abs_hz":
|
| 227 |
-
"median_abs_hz":
|
| 228 |
"observed_mean_hz": NaN,
|
| 229 |
-
"p95_abs_hz":
|
| 230 |
"pair_count": 0
|
| 231 |
},
|
| 232 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 233 |
-
"bias_hz":
|
| 234 |
"expected_mean_hz": NaN,
|
| 235 |
-
"mae_hz":
|
| 236 |
-
"max_abs_hz":
|
| 237 |
-
"median_abs_hz":
|
| 238 |
"observed_mean_hz": NaN,
|
| 239 |
-
"p95_abs_hz":
|
| 240 |
"pair_count": 0
|
| 241 |
},
|
| 242 |
"town": "Town03"
|
| 243 |
},
|
| 244 |
{
|
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| 245 |
"direct_multiplicity_max": 0,
|
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| 246 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 247 |
-
"bias_hz":
|
| 248 |
-
"mae_hz":
|
| 249 |
-
"max_abs_hz":
|
| 250 |
-
"median_abs_hz":
|
| 251 |
-
"p95_abs_hz":
|
| 252 |
"pair_count": 0
|
| 253 |
},
|
| 254 |
"profile": "core",
|
| 255 |
"sample_id": "Req_Town03_dynamic_60kmh_t009_rfcarlasafe01",
|
| 256 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 257 |
-
"bias_hz":
|
| 258 |
"expected_mean_hz": NaN,
|
| 259 |
-
"mae_hz":
|
| 260 |
-
"max_abs_hz":
|
| 261 |
-
"median_abs_hz":
|
| 262 |
"observed_mean_hz": NaN,
|
| 263 |
-
"p95_abs_hz":
|
| 264 |
"pair_count": 0
|
| 265 |
},
|
| 266 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 267 |
-
"bias_hz":
|
| 268 |
"expected_mean_hz": NaN,
|
| 269 |
-
"mae_hz":
|
| 270 |
-
"max_abs_hz":
|
| 271 |
-
"median_abs_hz":
|
| 272 |
"observed_mean_hz": NaN,
|
| 273 |
-
"p95_abs_hz":
|
| 274 |
"pair_count": 0
|
| 275 |
},
|
| 276 |
"town": "Town03"
|
| 277 |
},
|
| 278 |
{
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| 279 |
"direct_multiplicity_max": 0,
|
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| 280 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 281 |
-
"bias_hz":
|
| 282 |
-
"mae_hz":
|
| 283 |
-
"max_abs_hz":
|
| 284 |
-
"median_abs_hz":
|
| 285 |
-
"p95_abs_hz":
|
| 286 |
"pair_count": 0
|
| 287 |
},
|
| 288 |
"profile": "multipath",
|
| 289 |
"sample_id": "Req_Town04_dynamic_60kmh_t000_rfmain",
|
| 290 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 291 |
-
"bias_hz":
|
| 292 |
"expected_mean_hz": NaN,
|
| 293 |
-
"mae_hz":
|
| 294 |
-
"max_abs_hz":
|
| 295 |
-
"median_abs_hz":
|
| 296 |
"observed_mean_hz": NaN,
|
| 297 |
-
"p95_abs_hz":
|
| 298 |
"pair_count": 0
|
| 299 |
},
|
| 300 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 301 |
-
"bias_hz":
|
| 302 |
"expected_mean_hz": NaN,
|
| 303 |
-
"mae_hz":
|
| 304 |
-
"max_abs_hz":
|
| 305 |
-
"median_abs_hz":
|
| 306 |
"observed_mean_hz": NaN,
|
| 307 |
-
"p95_abs_hz":
|
| 308 |
"pair_count": 0
|
| 309 |
},
|
| 310 |
"town": "Town04"
|
| 311 |
},
|
| 312 |
{
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| 313 |
"direct_multiplicity_max": 1,
|
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|
| 314 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 315 |
"bias_hz": 0.19862045304662435,
|
| 316 |
"mae_hz": 0.3230334144879339,
|
|
@@ -344,7 +1023,73 @@
|
|
| 344 |
"town": "Town04"
|
| 345 |
},
|
| 346 |
{
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| 347 |
"direct_multiplicity_max": 1,
|
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|
| 348 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 349 |
"bias_hz": 0.33354087442509894,
|
| 350 |
"mae_hz": 0.3663986259794079,
|
|
@@ -378,7 +1123,73 @@
|
|
| 378 |
"town": "Town05"
|
| 379 |
},
|
| 380 |
{
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| 381 |
"direct_multiplicity_max": 1,
|
|
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|
| 382 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 383 |
"bias_hz": -1.7058159361523086,
|
| 384 |
"mae_hz": 1.7408948622779443,
|
|
@@ -412,7 +1223,73 @@
|
|
| 412 |
"town": "Town05"
|
| 413 |
},
|
| 414 |
{
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|
| 415 |
"direct_multiplicity_max": 1,
|
|
|
|
|
|
|
|
|
|
| 416 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 417 |
"bias_hz": 1.0863362667876695,
|
| 418 |
"mae_hz": 1.0864719952587398,
|
|
@@ -446,7 +1323,73 @@
|
|
| 446 |
"town": "Town06"
|
| 447 |
},
|
| 448 |
{
|
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|
| 449 |
"direct_multiplicity_max": 1,
|
|
|
|
|
|
|
|
|
|
| 450 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 451 |
"bias_hz": 0.1093556518534658,
|
| 452 |
"mae_hz": 0.5182785799713547,
|
|
@@ -480,41 +1423,173 @@
|
|
| 480 |
"town": "Town06"
|
| 481 |
},
|
| 482 |
{
|
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|
|
| 483 |
"direct_multiplicity_max": 0,
|
|
|
|
|
|
|
|
|
|
| 484 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 485 |
-
"bias_hz":
|
| 486 |
-
"mae_hz":
|
| 487 |
-
"max_abs_hz":
|
| 488 |
-
"median_abs_hz":
|
| 489 |
-
"p95_abs_hz":
|
| 490 |
"pair_count": 0
|
| 491 |
},
|
| 492 |
"profile": "core",
|
| 493 |
"sample_id": "Req_Town07_dynamic_60kmh_t000_rfmain",
|
| 494 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 495 |
-
"bias_hz":
|
| 496 |
"expected_mean_hz": NaN,
|
| 497 |
-
"mae_hz":
|
| 498 |
-
"max_abs_hz":
|
| 499 |
-
"median_abs_hz":
|
| 500 |
"observed_mean_hz": NaN,
|
| 501 |
-
"p95_abs_hz":
|
| 502 |
"pair_count": 0
|
| 503 |
},
|
| 504 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 505 |
-
"bias_hz":
|
| 506 |
"expected_mean_hz": NaN,
|
| 507 |
-
"mae_hz":
|
| 508 |
-
"max_abs_hz":
|
| 509 |
-
"median_abs_hz":
|
| 510 |
"observed_mean_hz": NaN,
|
| 511 |
-
"p95_abs_hz":
|
| 512 |
"pair_count": 0
|
| 513 |
},
|
| 514 |
"town": "Town07"
|
| 515 |
},
|
| 516 |
{
|
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|
| 517 |
"direct_multiplicity_max": 1,
|
|
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|
| 518 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 519 |
"bias_hz": 1.7644763722050534,
|
| 520 |
"mae_hz": 1.7644763722050534,
|
|
@@ -548,41 +1623,173 @@
|
|
| 548 |
"town": "Town07"
|
| 549 |
},
|
| 550 |
{
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|
| 551 |
"direct_multiplicity_max": 0,
|
|
|
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|
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|
|
|
|
| 552 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 553 |
-
"bias_hz":
|
| 554 |
-
"mae_hz":
|
| 555 |
-
"max_abs_hz":
|
| 556 |
-
"median_abs_hz":
|
| 557 |
-
"p95_abs_hz":
|
| 558 |
"pair_count": 0
|
| 559 |
},
|
| 560 |
"profile": "scattering_mild",
|
| 561 |
"sample_id": "Req_Town10HD_dynamic_60kmh_t001_rfmain",
|
| 562 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 563 |
-
"bias_hz":
|
| 564 |
"expected_mean_hz": NaN,
|
| 565 |
-
"mae_hz":
|
| 566 |
-
"max_abs_hz":
|
| 567 |
-
"median_abs_hz":
|
| 568 |
"observed_mean_hz": NaN,
|
| 569 |
-
"p95_abs_hz":
|
| 570 |
"pair_count": 0
|
| 571 |
},
|
| 572 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 573 |
-
"bias_hz":
|
| 574 |
"expected_mean_hz": NaN,
|
| 575 |
-
"mae_hz":
|
| 576 |
-
"max_abs_hz":
|
| 577 |
-
"median_abs_hz":
|
| 578 |
"observed_mean_hz": NaN,
|
| 579 |
-
"p95_abs_hz":
|
| 580 |
"pair_count": 0
|
| 581 |
},
|
| 582 |
"town": "Town10HD"
|
| 583 |
},
|
| 584 |
{
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| 585 |
"direct_multiplicity_max": 1,
|
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| 586 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 587 |
"bias_hz": 5.259215102611178,
|
| 588 |
"mae_hz": 5.259215102611178,
|
|
@@ -616,7 +1823,73 @@
|
|
| 616 |
"town": "Town10HD"
|
| 617 |
},
|
| 618 |
{
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| 619 |
"direct_multiplicity_max": 1,
|
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|
| 620 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 621 |
"bias_hz": -1.733019321357366,
|
| 622 |
"mae_hz": 6.777227009713237,
|
|
@@ -650,7 +1923,73 @@
|
|
| 650 |
"town": "Town11"
|
| 651 |
},
|
| 652 |
{
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| 653 |
"direct_multiplicity_max": 1,
|
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|
| 654 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 655 |
"bias_hz": -0.1697131620943258,
|
| 656 |
"mae_hz": 8.294031858143185,
|
|
@@ -684,7 +2023,73 @@
|
|
| 684 |
"town": "Town11"
|
| 685 |
},
|
| 686 |
{
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| 687 |
"direct_multiplicity_max": 1,
|
|
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|
|
| 688 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 689 |
"bias_hz": -0.10414106043746098,
|
| 690 |
"mae_hz": 0.9570452609147636,
|
|
@@ -718,7 +2123,73 @@
|
|
| 718 |
"town": "Town12"
|
| 719 |
},
|
| 720 |
{
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|
| 721 |
"direct_multiplicity_max": 1,
|
|
|
|
|
|
|
|
|
|
| 722 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 723 |
"bias_hz": -0.06689553023851282,
|
| 724 |
"mae_hz": 0.9401786883235674,
|
|
@@ -752,7 +2223,73 @@
|
|
| 752 |
"town": "Town12"
|
| 753 |
},
|
| 754 |
{
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|
| 755 |
"direct_multiplicity_max": 1,
|
|
|
|
|
|
|
|
|
|
| 756 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 757 |
"bias_hz": -2.2274451181548156,
|
| 758 |
"mae_hz": 3.203971947093018,
|
|
@@ -786,7 +2323,73 @@
|
|
| 786 |
"town": "Town13"
|
| 787 |
},
|
| 788 |
{
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|
| 789 |
"direct_multiplicity_max": 1,
|
|
|
|
|
|
|
|
|
|
| 790 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 791 |
"bias_hz": -0.07334066154706237,
|
| 792 |
"mae_hz": 3.6262482648676624,
|
|
@@ -820,82 +2423,217 @@
|
|
| 820 |
"town": "Town13"
|
| 821 |
},
|
| 822 |
{
|
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|
|
| 823 |
"direct_multiplicity_max": 0,
|
|
|
|
|
|
|
|
|
|
| 824 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 825 |
-
"bias_hz":
|
| 826 |
-
"mae_hz":
|
| 827 |
-
"max_abs_hz":
|
| 828 |
-
"median_abs_hz":
|
| 829 |
-
"p95_abs_hz":
|
| 830 |
"pair_count": 0
|
| 831 |
},
|
| 832 |
"profile": "scattering_medium",
|
| 833 |
"sample_id": "Req_Town15_dynamic_60kmh_t002_rfmain",
|
| 834 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 835 |
-
"bias_hz":
|
| 836 |
"expected_mean_hz": NaN,
|
| 837 |
-
"mae_hz":
|
| 838 |
-
"max_abs_hz":
|
| 839 |
-
"median_abs_hz":
|
| 840 |
"observed_mean_hz": NaN,
|
| 841 |
-
"p95_abs_hz":
|
| 842 |
"pair_count": 0
|
| 843 |
},
|
| 844 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 845 |
-
"bias_hz":
|
| 846 |
"expected_mean_hz": NaN,
|
| 847 |
-
"mae_hz":
|
| 848 |
-
"max_abs_hz":
|
| 849 |
-
"median_abs_hz":
|
| 850 |
"observed_mean_hz": NaN,
|
| 851 |
-
"p95_abs_hz":
|
| 852 |
"pair_count": 0
|
| 853 |
},
|
| 854 |
"town": "Town15"
|
| 855 |
},
|
| 856 |
{
|
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|
| 857 |
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"bias_hz": null,
|
| 2459 |
+
"mae_hz": null,
|
| 2460 |
+
"max_abs_hz": null,
|
| 2461 |
+
"median_abs_hz": null,
|
| 2462 |
+
"p95_abs_hz": null,
|
| 2463 |
+
"pair_count": 0,
|
| 2464 |
+
"relative_threshold": 1e-07,
|
| 2465 |
+
"retained_fraction": null
|
| 2466 |
+
},
|
| 2467 |
+
"1e-08": {
|
| 2468 |
+
"bias_hz": null,
|
| 2469 |
+
"mae_hz": null,
|
| 2470 |
+
"max_abs_hz": null,
|
| 2471 |
+
"median_abs_hz": null,
|
| 2472 |
+
"p95_abs_hz": null,
|
| 2473 |
+
"pair_count": 0,
|
| 2474 |
+
"relative_threshold": 1e-08,
|
| 2475 |
+
"retained_fraction": null
|
| 2476 |
+
},
|
| 2477 |
+
"nonzero": {
|
| 2478 |
+
"bias_hz": null,
|
| 2479 |
+
"mae_hz": null,
|
| 2480 |
+
"max_abs_hz": null,
|
| 2481 |
+
"median_abs_hz": null,
|
| 2482 |
+
"p95_abs_hz": null,
|
| 2483 |
+
"pair_count": 0,
|
| 2484 |
+
"relative_threshold": 0.0,
|
| 2485 |
+
"retained_fraction": null
|
| 2486 |
+
}
|
| 2487 |
+
}
|
| 2488 |
+
},
|
| 2489 |
"direct_multiplicity_max": 0,
|
| 2490 |
+
"direct_state_count": 0,
|
| 2491 |
+
"eligibility": "no_type0_direct_state",
|
| 2492 |
+
"finite_nonzero_center_state_count": 0,
|
| 2493 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 2494 |
+
"bias_hz": null,
|
| 2495 |
+
"mae_hz": null,
|
| 2496 |
+
"max_abs_hz": null,
|
| 2497 |
+
"median_abs_hz": null,
|
| 2498 |
+
"p95_abs_hz": null,
|
| 2499 |
"pair_count": 0
|
| 2500 |
},
|
| 2501 |
"profile": "scattering_medium",
|
| 2502 |
"sample_id": "Req_Town15_dynamic_60kmh_t002_rfmain",
|
| 2503 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 2504 |
+
"bias_hz": null,
|
| 2505 |
"expected_mean_hz": NaN,
|
| 2506 |
+
"mae_hz": null,
|
| 2507 |
+
"max_abs_hz": null,
|
| 2508 |
+
"median_abs_hz": null,
|
| 2509 |
"observed_mean_hz": NaN,
|
| 2510 |
+
"p95_abs_hz": null,
|
| 2511 |
"pair_count": 0
|
| 2512 |
},
|
| 2513 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 2514 |
+
"bias_hz": null,
|
| 2515 |
"expected_mean_hz": NaN,
|
| 2516 |
+
"mae_hz": null,
|
| 2517 |
+
"max_abs_hz": null,
|
| 2518 |
+
"median_abs_hz": null,
|
| 2519 |
"observed_mean_hz": NaN,
|
| 2520 |
+
"p95_abs_hz": null,
|
| 2521 |
"pair_count": 0
|
| 2522 |
},
|
| 2523 |
"town": "Town15"
|
| 2524 |
},
|
| 2525 |
{
|
| 2526 |
+
"adjacent_unique_type0_pair_candidates": 0,
|
| 2527 |
+
"ambiguous_direct_state_count": 0,
|
| 2528 |
+
"center_coefficient_magnitude_stability": {
|
| 2529 |
+
"episode_reference_max_abs": null,
|
| 2530 |
+
"pair_min_abs": {
|
| 2531 |
+
"count": 0,
|
| 2532 |
+
"max": null,
|
| 2533 |
+
"median": null,
|
| 2534 |
+
"min": null,
|
| 2535 |
+
"p01": null,
|
| 2536 |
+
"p95": null
|
| 2537 |
+
},
|
| 2538 |
+
"pair_min_relative_to_episode_max": {
|
| 2539 |
+
"count": 0,
|
| 2540 |
+
"max": null,
|
| 2541 |
+
"median": null,
|
| 2542 |
+
"min": null,
|
| 2543 |
+
"p01": null,
|
| 2544 |
+
"p95": null
|
| 2545 |
+
},
|
| 2546 |
+
"threshold_sensitivity_vs_position_increment": {
|
| 2547 |
+
"1e-06": {
|
| 2548 |
+
"bias_hz": null,
|
| 2549 |
+
"mae_hz": null,
|
| 2550 |
+
"max_abs_hz": null,
|
| 2551 |
+
"median_abs_hz": null,
|
| 2552 |
+
"p95_abs_hz": null,
|
| 2553 |
+
"pair_count": 0,
|
| 2554 |
+
"relative_threshold": 1e-06,
|
| 2555 |
+
"retained_fraction": null
|
| 2556 |
+
},
|
| 2557 |
+
"1e-07": {
|
| 2558 |
+
"bias_hz": null,
|
| 2559 |
+
"mae_hz": null,
|
| 2560 |
+
"max_abs_hz": null,
|
| 2561 |
+
"median_abs_hz": null,
|
| 2562 |
+
"p95_abs_hz": null,
|
| 2563 |
+
"pair_count": 0,
|
| 2564 |
+
"relative_threshold": 1e-07,
|
| 2565 |
+
"retained_fraction": null
|
| 2566 |
+
},
|
| 2567 |
+
"1e-08": {
|
| 2568 |
+
"bias_hz": null,
|
| 2569 |
+
"mae_hz": null,
|
| 2570 |
+
"max_abs_hz": null,
|
| 2571 |
+
"median_abs_hz": null,
|
| 2572 |
+
"p95_abs_hz": null,
|
| 2573 |
+
"pair_count": 0,
|
| 2574 |
+
"relative_threshold": 1e-08,
|
| 2575 |
+
"retained_fraction": null
|
| 2576 |
+
},
|
| 2577 |
+
"nonzero": {
|
| 2578 |
+
"bias_hz": null,
|
| 2579 |
+
"mae_hz": null,
|
| 2580 |
+
"max_abs_hz": null,
|
| 2581 |
+
"median_abs_hz": null,
|
| 2582 |
+
"p95_abs_hz": null,
|
| 2583 |
+
"pair_count": 0,
|
| 2584 |
+
"relative_threshold": 0.0,
|
| 2585 |
+
"retained_fraction": null
|
| 2586 |
+
}
|
| 2587 |
+
}
|
| 2588 |
+
},
|
| 2589 |
"direct_multiplicity_max": 0,
|
| 2590 |
+
"direct_state_count": 0,
|
| 2591 |
+
"eligibility": "no_type0_direct_state",
|
| 2592 |
+
"finite_nonzero_center_state_count": 0,
|
| 2593 |
"legacy_rx0_tx0_vs_center_geometry": {
|
| 2594 |
+
"bias_hz": null,
|
| 2595 |
+
"mae_hz": null,
|
| 2596 |
+
"max_abs_hz": null,
|
| 2597 |
+
"median_abs_hz": null,
|
| 2598 |
+
"p95_abs_hz": null,
|
| 2599 |
"pair_count": 0
|
| 2600 |
},
|
| 2601 |
"profile": "core",
|
| 2602 |
"sample_id": "Req_Town15_dynamic_60kmh_t020_rfsupp03",
|
| 2603 |
"symmetric_center_equivalent_vs_position_increment_geometry": {
|
| 2604 |
+
"bias_hz": null,
|
| 2605 |
"expected_mean_hz": NaN,
|
| 2606 |
+
"mae_hz": null,
|
| 2607 |
+
"max_abs_hz": null,
|
| 2608 |
+
"median_abs_hz": null,
|
| 2609 |
"observed_mean_hz": NaN,
|
| 2610 |
+
"p95_abs_hz": null,
|
| 2611 |
"pair_count": 0
|
| 2612 |
},
|
| 2613 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": {
|
| 2614 |
+
"bias_hz": null,
|
| 2615 |
"expected_mean_hz": NaN,
|
| 2616 |
+
"mae_hz": null,
|
| 2617 |
+
"max_abs_hz": null,
|
| 2618 |
+
"median_abs_hz": null,
|
| 2619 |
"observed_mean_hz": NaN,
|
| 2620 |
+
"p95_abs_hz": null,
|
| 2621 |
"pair_count": 0
|
| 2622 |
},
|
| 2623 |
"town": "Town15"
|
| 2624 |
}
|
| 2625 |
],
|
| 2626 |
"legacy": {
|
| 2627 |
+
"eligible_episode_count": 14,
|
| 2628 |
+
"episode_count": 14,
|
| 2629 |
"episode_mean_bias_hz": 0.3463717186273215,
|
| 2630 |
"episode_mean_mae_hz": 2.8161464244758645,
|
| 2631 |
"episode_mean_max_abs_hz": 13.015620437622681,
|
| 2632 |
"episode_mean_median_abs_hz": 1.9278234035924942,
|
| 2633 |
+
"episode_mean_p95_abs_hz": 9.447052586557586,
|
| 2634 |
+
"excluded_episode_count": 10,
|
| 2635 |
+
"pair_count": 24386,
|
| 2636 |
+
"selected_episode_count": 24
|
| 2637 |
},
|
| 2638 |
"scope": "24 frozen 60-km/h episodes; all 2,000 states per episode",
|
| 2639 |
"semantic_note": "Legacy Rx0/Tx0 phase is an element-pair quantity. The symmetric product cancels array offsets and is compared with endpoint-center geometry.",
|
metadata/paper_reproducibility/scripts/analyze_scene2wave_array_doppler.py
CHANGED
|
@@ -32,6 +32,7 @@ TX_CENTER_INDICES = (15, 16)
|
|
| 32 |
RX_CENTER_INDICES = (0, 1)
|
| 33 |
BOOTSTRAP_REPLICATES = 10_000
|
| 34 |
BOOTSTRAP_SEED = 20260824
|
|
|
|
| 35 |
|
| 36 |
|
| 37 |
def parse_args() -> argparse.Namespace:
|
|
@@ -54,9 +55,14 @@ def channel_files(sample_root: Path) -> list[Path]:
|
|
| 54 |
return sorted(directories[0].glob("*_paths.npz"), key=frame_number)
|
| 55 |
|
| 56 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 57 |
def normalized_symmetric_product(amplitudes: np.ndarray, path_index: int) -> complex:
|
| 58 |
"""Return the fourth-power array-center phasor for one path."""
|
| 59 |
-
values =
|
| 60 |
magnitudes = np.abs(values)
|
| 61 |
if values.size != 4 or np.any(~np.isfinite(values)) or np.any(magnitudes == 0):
|
| 62 |
return complex(np.nan, np.nan)
|
|
@@ -92,6 +98,65 @@ def position_increment_doppler(previous_distance_m: float, current_distance_m: f
|
|
| 92 |
return -carrier_hz * (current_distance_m - previous_distance_m) * sampling_rate_hz / LIGHT_SPEED_MPS
|
| 93 |
|
| 94 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 95 |
def _audit_episode(task: tuple[dict[str, Any], str]) -> dict[str, Any]:
|
| 96 |
row, dataset_root_text = task
|
| 97 |
sample_root = Path(dataset_root_text) / row["relative_path"]
|
|
@@ -105,8 +170,14 @@ def _audit_episode(task: tuple[dict[str, Any], str]) -> dict[str, Any]:
|
|
| 105 |
center_observed: list[float] = []
|
| 106 |
expected_values: list[float] = []
|
| 107 |
position_expected_values: list[float] = []
|
| 108 |
-
|
|
|
|
| 109 |
multiplicity_max = 0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 110 |
for path in files:
|
| 111 |
with np.load(path, allow_pickle=False) as data:
|
| 112 |
amplitudes = np.asarray(data["a"])[0, 0, :, 0, :, :, 0]
|
|
@@ -116,10 +187,27 @@ def _audit_episode(task: tuple[dict[str, Any], str]) -> dict[str, Any]:
|
|
| 116 |
direct = np.flatnonzero(types == 0)
|
| 117 |
multiplicity_max = max(multiplicity_max, int(direct.size))
|
| 118 |
if direct.size != 1:
|
|
|
|
| 119 |
previous = None
|
| 120 |
continue
|
|
|
|
| 121 |
index = int(direct[0])
|
| 122 |
legacy = complex(amplitudes[0, 0, index])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 123 |
center = normalized_symmetric_product(amplitudes, index)
|
| 124 |
expected = geometry_doppler(data)
|
| 125 |
tx_position = np.asarray(data["tx_position_m"], dtype=np.float64).reshape(3)
|
|
@@ -129,6 +217,7 @@ def _audit_episode(task: tuple[dict[str, Any], str]) -> dict[str, Any]:
|
|
| 129 |
frame = frame_number(path)
|
| 130 |
rate = float(np.asarray(data["csi_sampling_frequency_hz"]).item())
|
| 131 |
if previous is not None and frame == previous[0] + 1:
|
|
|
|
| 132 |
legacy_hz = legacy_phase_doppler(previous[1], legacy, rate)
|
| 133 |
center_hz = center_phase_doppler(previous[2], center, rate)
|
| 134 |
expected_hz = 0.5 * (previous[3] + expected)
|
|
@@ -140,50 +229,74 @@ def _audit_episode(task: tuple[dict[str, Any], str]) -> dict[str, Any]:
|
|
| 140 |
center_observed.append(center_hz)
|
| 141 |
expected_values.append(expected_hz)
|
| 142 |
position_expected_values.append(position_expected_hz)
|
| 143 |
-
|
| 144 |
-
|
| 145 |
-
def compact(values: list[float]) -> dict[str, float | int]:
|
| 146 |
-
array = np.asarray(values, dtype=np.float64)
|
| 147 |
-
absolute = np.abs(array)
|
| 148 |
-
return {
|
| 149 |
-
"pair_count": int(array.size),
|
| 150 |
-
"bias_hz": float(np.mean(array)) if array.size else math.nan,
|
| 151 |
-
"mae_hz": float(np.mean(absolute)) if array.size else math.nan,
|
| 152 |
-
"median_abs_hz": float(np.median(absolute)) if array.size else math.nan,
|
| 153 |
-
"p95_abs_hz": float(np.percentile(absolute, 95)) if array.size else math.nan,
|
| 154 |
-
"max_abs_hz": float(np.max(absolute)) if array.size else math.nan,
|
| 155 |
-
}
|
| 156 |
|
| 157 |
-
center_stats =
|
| 158 |
observed = np.asarray(center_observed, dtype=np.float64)
|
| 159 |
expected = np.asarray(expected_values, dtype=np.float64)
|
| 160 |
center_stats["observed_mean_hz"] = float(np.mean(observed)) if observed.size else math.nan
|
| 161 |
center_stats["expected_mean_hz"] = float(np.mean(expected)) if expected.size else math.nan
|
| 162 |
-
center_position_stats =
|
| 163 |
position_expected = np.asarray(position_expected_values, dtype=np.float64)
|
| 164 |
center_position_stats["observed_mean_hz"] = float(np.mean(observed)) if observed.size else math.nan
|
| 165 |
center_position_stats["expected_mean_hz"] = float(np.mean(position_expected)) if position_expected.size else math.nan
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 166 |
return {
|
| 167 |
"sample_id": row["sample_id"],
|
| 168 |
"town": row["town"],
|
| 169 |
"profile": row["profile"],
|
| 170 |
"direct_multiplicity_max": multiplicity_max,
|
| 171 |
-
"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 172 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": center_stats,
|
| 173 |
"symmetric_center_equivalent_vs_position_increment_geometry": center_position_stats,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 174 |
}
|
| 175 |
|
| 176 |
|
| 177 |
def aggregate(rows: list[dict[str, Any]], key: str) -> dict[str, Any]:
|
| 178 |
metrics = ("bias_hz", "mae_hz", "median_abs_hz", "p95_abs_hz", "max_abs_hz")
|
| 179 |
pair_count = sum(int(row[key]["pair_count"]) for row in rows)
|
| 180 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 181 |
for metric in metrics:
|
| 182 |
-
values = np.asarray([row[key][metric] for row in
|
| 183 |
-
result[f"episode_mean_{metric}"] = float(np.
|
| 184 |
if key.startswith("symmetric"):
|
| 185 |
-
observed = np.asarray([row[key]["observed_mean_hz"] for row in
|
| 186 |
-
expected = np.asarray([row[key]["expected_mean_hz"] for row in
|
| 187 |
finite = np.isfinite(observed) & np.isfinite(expected)
|
| 188 |
observed = observed[finite]
|
| 189 |
expected = expected[finite]
|
|
@@ -211,10 +324,78 @@ def aggregate(rows: list[dict[str, Any]], key: str) -> dict[str, Any]:
|
|
| 211 |
"slope": [float(value) for value in np.percentile(fits[:, 0], [2.5, 97.5])],
|
| 212 |
"intercept_hz": [float(value) for value in np.percentile(fits[:, 1], [2.5, 97.5])],
|
| 213 |
}
|
| 214 |
-
result["pooled_max_abs_hz"] = float(np.
|
| 215 |
return result
|
| 216 |
|
| 217 |
|
|
|
|
|
|
|
|
|
|
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|
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| 218 |
def plot_center_position_parity(rows: list[dict[str, Any]], destination: Path) -> None:
|
| 219 |
key = "symmetric_center_equivalent_vs_position_increment_geometry"
|
| 220 |
expected = np.asarray([row[key]["expected_mean_hz"] for row in rows], dtype=np.float64)
|
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@@ -270,9 +451,11 @@ def main() -> int:
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|
| 270 |
"rx_normalized_y_lambda": [-0.25, 0.25],
|
| 271 |
},
|
| 272 |
"semantic_note": "Legacy Rx0/Tx0 phase is an element-pair quantity. The symmetric product cancels array offsets and is compared with endpoint-center geometry.",
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|
| 273 |
"legacy": aggregate(rows, "legacy_rx0_tx0_vs_center_geometry"),
|
| 274 |
"center_equivalent_vs_stored_velocity": aggregate(rows, "symmetric_center_equivalent_vs_stored_velocity_geometry"),
|
| 275 |
"center_equivalent_vs_position_increment": aggregate(rows, "symmetric_center_equivalent_vs_position_increment_geometry"),
|
|
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|
| 276 |
"episodes": rows,
|
| 277 |
}
|
| 278 |
args.output_root.mkdir(parents=True, exist_ok=True)
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@@ -288,6 +471,8 @@ def main() -> int:
|
|
| 288 |
"",
|
| 289 |
report["scope"],
|
| 290 |
"",
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|
| 291 |
"| Definition | Adjacent pairs | Episode-mean MAE | Episode-mean bias | Episode-mean P95 abs. |",
|
| 292 |
"|---|---:|---:|---:|---:|",
|
| 293 |
f"| Legacy Rx0/Tx0 vs endpoint center | {legacy['pair_count']:,} | {legacy['episode_mean_mae_hz']:.4f} Hz | {legacy['episode_mean_bias_hz']:.4f} Hz | {legacy['episode_mean_p95_abs_hz']:.4f} Hz |",
|
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@@ -297,6 +482,15 @@ def main() -> int:
|
|
| 297 |
f"Center-equivalent episode-mean Pearson correlation: {center_position['episode_mean_pearson_r']:.6f} against adjacent-position range rate ({center['episode_mean_pearson_r']:.6f} against stored velocity).",
|
| 298 |
f"The position-derived episode-mean fit has slope {center_position['episode_mean_linear_fit']['slope']:.8f} and intercept {center_position['episode_mean_linear_fit']['intercept_hz']:.6f} Hz; its pooled maximum absolute pair error is {center_position['pooled_max_abs_hz']:.4f} Hz.",
|
| 299 |
"",
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| 300 |
"The legacy definition is retained only as a diagnostic. Paper claims should use the center-equivalent result or explicitly name the antenna element pair.",
|
| 301 |
]
|
| 302 |
(args.output_root / "README.md").write_text("\n".join(lines) + "\n", encoding="utf-8")
|
|
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|
| 32 |
RX_CENTER_INDICES = (0, 1)
|
| 33 |
BOOTSTRAP_REPLICATES = 10_000
|
| 34 |
BOOTSTRAP_SEED = 20260824
|
| 35 |
+
MAGNITUDE_RELATIVE_THRESHOLDS = (0.0, 1.0e-8, 1.0e-7, 1.0e-6)
|
| 36 |
|
| 37 |
|
| 38 |
def parse_args() -> argparse.Namespace:
|
|
|
|
| 55 |
return sorted(directories[0].glob("*_paths.npz"), key=frame_number)
|
| 56 |
|
| 57 |
|
| 58 |
+
def symmetric_center_values(amplitudes: np.ndarray, path_index: int) -> np.ndarray:
|
| 59 |
+
"""Return the four central Tx/Rx coefficients used by the center audit."""
|
| 60 |
+
return amplitudes[np.ix_(RX_CENTER_INDICES, TX_CENTER_INDICES, [path_index])].reshape(-1)
|
| 61 |
+
|
| 62 |
+
|
| 63 |
def normalized_symmetric_product(amplitudes: np.ndarray, path_index: int) -> complex:
|
| 64 |
"""Return the fourth-power array-center phasor for one path."""
|
| 65 |
+
values = symmetric_center_values(amplitudes, path_index)
|
| 66 |
magnitudes = np.abs(values)
|
| 67 |
if values.size != 4 or np.any(~np.isfinite(values)) or np.any(magnitudes == 0):
|
| 68 |
return complex(np.nan, np.nan)
|
|
|
|
| 98 |
return -carrier_hz * (current_distance_m - previous_distance_m) * sampling_rate_hz / LIGHT_SPEED_MPS
|
| 99 |
|
| 100 |
|
| 101 |
+
def compact_residuals(values: np.ndarray | list[float]) -> dict[str, float | int | None]:
|
| 102 |
+
array = np.asarray(values, dtype=np.float64)
|
| 103 |
+
absolute = np.abs(array)
|
| 104 |
+
if not array.size:
|
| 105 |
+
return {
|
| 106 |
+
"pair_count": 0,
|
| 107 |
+
"bias_hz": None,
|
| 108 |
+
"mae_hz": None,
|
| 109 |
+
"median_abs_hz": None,
|
| 110 |
+
"p95_abs_hz": None,
|
| 111 |
+
"max_abs_hz": None,
|
| 112 |
+
}
|
| 113 |
+
return {
|
| 114 |
+
"pair_count": int(array.size),
|
| 115 |
+
"bias_hz": float(np.mean(array)),
|
| 116 |
+
"mae_hz": float(np.mean(absolute)),
|
| 117 |
+
"median_abs_hz": float(np.median(absolute)),
|
| 118 |
+
"p95_abs_hz": float(np.percentile(absolute, 95)),
|
| 119 |
+
"max_abs_hz": float(np.max(absolute)),
|
| 120 |
+
}
|
| 121 |
+
|
| 122 |
+
|
| 123 |
+
def compact_positive(values: np.ndarray | list[float]) -> dict[str, float | int | None]:
|
| 124 |
+
array = np.asarray(values, dtype=np.float64)
|
| 125 |
+
array = array[np.isfinite(array)]
|
| 126 |
+
if not array.size:
|
| 127 |
+
return {"count": 0, "min": None, "p01": None, "median": None, "p95": None, "max": None}
|
| 128 |
+
return {
|
| 129 |
+
"count": int(array.size),
|
| 130 |
+
"min": float(np.min(array)),
|
| 131 |
+
"p01": float(np.percentile(array, 1)),
|
| 132 |
+
"median": float(np.median(array)),
|
| 133 |
+
"p95": float(np.percentile(array, 95)),
|
| 134 |
+
"max": float(np.max(array)),
|
| 135 |
+
}
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
def magnitude_threshold_sensitivity(
|
| 139 |
+
residuals: np.ndarray | list[float],
|
| 140 |
+
pair_min_relative_magnitude: np.ndarray | list[float],
|
| 141 |
+
) -> dict[str, dict[str, float | int | None]]:
|
| 142 |
+
residual_array = np.asarray(residuals, dtype=np.float64)
|
| 143 |
+
relative = np.asarray(pair_min_relative_magnitude, dtype=np.float64)
|
| 144 |
+
if residual_array.shape != relative.shape:
|
| 145 |
+
raise ValueError("Residual and magnitude arrays must have identical shapes")
|
| 146 |
+
output: dict[str, dict[str, float | int | None]] = {}
|
| 147 |
+
baseline_count = int(residual_array.size)
|
| 148 |
+
for threshold in MAGNITUDE_RELATIVE_THRESHOLDS:
|
| 149 |
+
key = "nonzero" if threshold == 0.0 else f"{threshold:.0e}"
|
| 150 |
+
mask = np.isfinite(relative) & (relative > threshold)
|
| 151 |
+
summary = compact_residuals(residual_array[mask])
|
| 152 |
+
summary["relative_threshold"] = float(threshold)
|
| 153 |
+
summary["retained_fraction"] = (
|
| 154 |
+
float(summary["pair_count"]) / baseline_count if baseline_count else None
|
| 155 |
+
)
|
| 156 |
+
output[key] = summary
|
| 157 |
+
return output
|
| 158 |
+
|
| 159 |
+
|
| 160 |
def _audit_episode(task: tuple[dict[str, Any], str]) -> dict[str, Any]:
|
| 161 |
row, dataset_root_text = task
|
| 162 |
sample_root = Path(dataset_root_text) / row["relative_path"]
|
|
|
|
| 170 |
center_observed: list[float] = []
|
| 171 |
expected_values: list[float] = []
|
| 172 |
position_expected_values: list[float] = []
|
| 173 |
+
pair_min_center_magnitudes: list[float] = []
|
| 174 |
+
previous: tuple[int, complex, complex, float, float, float, float] | None = None
|
| 175 |
multiplicity_max = 0
|
| 176 |
+
direct_state_count = 0
|
| 177 |
+
finite_center_state_count = 0
|
| 178 |
+
adjacent_direct_candidate_count = 0
|
| 179 |
+
ambiguous_direct_state_count = 0
|
| 180 |
+
center_reference_max_abs = 0.0
|
| 181 |
for path in files:
|
| 182 |
with np.load(path, allow_pickle=False) as data:
|
| 183 |
amplitudes = np.asarray(data["a"])[0, 0, :, 0, :, :, 0]
|
|
|
|
| 187 |
direct = np.flatnonzero(types == 0)
|
| 188 |
multiplicity_max = max(multiplicity_max, int(direct.size))
|
| 189 |
if direct.size != 1:
|
| 190 |
+
ambiguous_direct_state_count += int(direct.size > 1)
|
| 191 |
previous = None
|
| 192 |
continue
|
| 193 |
+
direct_state_count += 1
|
| 194 |
index = int(direct[0])
|
| 195 |
legacy = complex(amplitudes[0, 0, index])
|
| 196 |
+
center_values = symmetric_center_values(amplitudes, index)
|
| 197 |
+
center_magnitudes = np.abs(center_values)
|
| 198 |
+
finite_center = bool(
|
| 199 |
+
center_values.size == 4
|
| 200 |
+
and np.all(np.isfinite(center_values))
|
| 201 |
+
and np.all(center_magnitudes > 0)
|
| 202 |
+
)
|
| 203 |
+
if finite_center:
|
| 204 |
+
finite_center_state_count += 1
|
| 205 |
+
center_reference_max_abs = max(
|
| 206 |
+
center_reference_max_abs, float(np.max(center_magnitudes))
|
| 207 |
+
)
|
| 208 |
+
center_min_abs = float(np.min(center_magnitudes))
|
| 209 |
+
else:
|
| 210 |
+
center_min_abs = math.nan
|
| 211 |
center = normalized_symmetric_product(amplitudes, index)
|
| 212 |
expected = geometry_doppler(data)
|
| 213 |
tx_position = np.asarray(data["tx_position_m"], dtype=np.float64).reshape(3)
|
|
|
|
| 217 |
frame = frame_number(path)
|
| 218 |
rate = float(np.asarray(data["csi_sampling_frequency_hz"]).item())
|
| 219 |
if previous is not None and frame == previous[0] + 1:
|
| 220 |
+
adjacent_direct_candidate_count += 1
|
| 221 |
legacy_hz = legacy_phase_doppler(previous[1], legacy, rate)
|
| 222 |
center_hz = center_phase_doppler(previous[2], center, rate)
|
| 223 |
expected_hz = 0.5 * (previous[3] + expected)
|
|
|
|
| 229 |
center_observed.append(center_hz)
|
| 230 |
expected_values.append(expected_hz)
|
| 231 |
position_expected_values.append(position_expected_hz)
|
| 232 |
+
pair_min_center_magnitudes.append(min(previous[6], center_min_abs))
|
| 233 |
+
previous = (frame, legacy, center, expected, distance_m, carrier_hz, center_min_abs)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 234 |
|
| 235 |
+
center_stats = compact_residuals(center_residuals)
|
| 236 |
observed = np.asarray(center_observed, dtype=np.float64)
|
| 237 |
expected = np.asarray(expected_values, dtype=np.float64)
|
| 238 |
center_stats["observed_mean_hz"] = float(np.mean(observed)) if observed.size else math.nan
|
| 239 |
center_stats["expected_mean_hz"] = float(np.mean(expected)) if expected.size else math.nan
|
| 240 |
+
center_position_stats = compact_residuals(center_position_residuals)
|
| 241 |
position_expected = np.asarray(position_expected_values, dtype=np.float64)
|
| 242 |
center_position_stats["observed_mean_hz"] = float(np.mean(observed)) if observed.size else math.nan
|
| 243 |
center_position_stats["expected_mean_hz"] = float(np.mean(position_expected)) if position_expected.size else math.nan
|
| 244 |
+
pair_min_abs = np.asarray(pair_min_center_magnitudes, dtype=np.float64)
|
| 245 |
+
pair_min_relative = (
|
| 246 |
+
pair_min_abs / center_reference_max_abs
|
| 247 |
+
if center_reference_max_abs > 0.0
|
| 248 |
+
else np.full(pair_min_abs.shape, np.nan, dtype=np.float64)
|
| 249 |
+
)
|
| 250 |
+
pair_count = int(center_position_stats["pair_count"])
|
| 251 |
+
if pair_count:
|
| 252 |
+
eligibility = "eligible"
|
| 253 |
+
elif direct_state_count == 0:
|
| 254 |
+
eligibility = "no_type0_direct_state"
|
| 255 |
+
elif adjacent_direct_candidate_count == 0:
|
| 256 |
+
eligibility = "no_adjacent_unique_type0_pair"
|
| 257 |
+
else:
|
| 258 |
+
eligibility = "no_finite_nonzero_center_pair"
|
| 259 |
return {
|
| 260 |
"sample_id": row["sample_id"],
|
| 261 |
"town": row["town"],
|
| 262 |
"profile": row["profile"],
|
| 263 |
"direct_multiplicity_max": multiplicity_max,
|
| 264 |
+
"direct_state_count": direct_state_count,
|
| 265 |
+
"finite_nonzero_center_state_count": finite_center_state_count,
|
| 266 |
+
"adjacent_unique_type0_pair_candidates": adjacent_direct_candidate_count,
|
| 267 |
+
"ambiguous_direct_state_count": ambiguous_direct_state_count,
|
| 268 |
+
"eligibility": eligibility,
|
| 269 |
+
"legacy_rx0_tx0_vs_center_geometry": compact_residuals(legacy_residuals),
|
| 270 |
"symmetric_center_equivalent_vs_stored_velocity_geometry": center_stats,
|
| 271 |
"symmetric_center_equivalent_vs_position_increment_geometry": center_position_stats,
|
| 272 |
+
"center_coefficient_magnitude_stability": {
|
| 273 |
+
"episode_reference_max_abs": center_reference_max_abs or None,
|
| 274 |
+
"pair_min_abs": compact_positive(pair_min_abs),
|
| 275 |
+
"pair_min_relative_to_episode_max": compact_positive(pair_min_relative),
|
| 276 |
+
"threshold_sensitivity_vs_position_increment": magnitude_threshold_sensitivity(
|
| 277 |
+
center_position_residuals, pair_min_relative
|
| 278 |
+
),
|
| 279 |
+
},
|
| 280 |
}
|
| 281 |
|
| 282 |
|
| 283 |
def aggregate(rows: list[dict[str, Any]], key: str) -> dict[str, Any]:
|
| 284 |
metrics = ("bias_hz", "mae_hz", "median_abs_hz", "p95_abs_hz", "max_abs_hz")
|
| 285 |
pair_count = sum(int(row[key]["pair_count"]) for row in rows)
|
| 286 |
+
eligible_rows = [row for row in rows if int(row[key]["pair_count"]) > 0]
|
| 287 |
+
result: dict[str, Any] = {
|
| 288 |
+
"selected_episode_count": len(rows),
|
| 289 |
+
"episode_count": len(eligible_rows),
|
| 290 |
+
"eligible_episode_count": len(eligible_rows),
|
| 291 |
+
"excluded_episode_count": len(rows) - len(eligible_rows),
|
| 292 |
+
"pair_count": pair_count,
|
| 293 |
+
}
|
| 294 |
for metric in metrics:
|
| 295 |
+
values = np.asarray([row[key][metric] for row in eligible_rows], dtype=np.float64)
|
| 296 |
+
result[f"episode_mean_{metric}"] = float(np.mean(values)) if values.size else None
|
| 297 |
if key.startswith("symmetric"):
|
| 298 |
+
observed = np.asarray([row[key]["observed_mean_hz"] for row in eligible_rows], dtype=np.float64)
|
| 299 |
+
expected = np.asarray([row[key]["expected_mean_hz"] for row in eligible_rows], dtype=np.float64)
|
| 300 |
finite = np.isfinite(observed) & np.isfinite(expected)
|
| 301 |
observed = observed[finite]
|
| 302 |
expected = expected[finite]
|
|
|
|
| 324 |
"slope": [float(value) for value in np.percentile(fits[:, 0], [2.5, 97.5])],
|
| 325 |
"intercept_hz": [float(value) for value in np.percentile(fits[:, 1], [2.5, 97.5])],
|
| 326 |
}
|
| 327 |
+
result["pooled_max_abs_hz"] = float(np.max([row[key]["max_abs_hz"] for row in eligible_rows]))
|
| 328 |
return result
|
| 329 |
|
| 330 |
|
| 331 |
+
def eligibility_coverage(rows: list[dict[str, Any]]) -> dict[str, Any]:
|
| 332 |
+
eligible = [row for row in rows if row["eligibility"] == "eligible"]
|
| 333 |
+
excluded = [row for row in rows if row["eligibility"] != "eligible"]
|
| 334 |
+
return {
|
| 335 |
+
"selected_episode_count": len(rows),
|
| 336 |
+
"eligible_episode_count": len(eligible),
|
| 337 |
+
"excluded_episode_count": len(excluded),
|
| 338 |
+
"eligible_towns": sorted({row["town"] for row in eligible}),
|
| 339 |
+
"eligible_town_count": len({row["town"] for row in eligible}),
|
| 340 |
+
"eligible_profiles": sorted({row["profile"] for row in eligible}),
|
| 341 |
+
"eligible_profile_count": len({row["profile"] for row in eligible}),
|
| 342 |
+
"eligible_sample_ids": [row["sample_id"] for row in eligible],
|
| 343 |
+
"excluded": [
|
| 344 |
+
{
|
| 345 |
+
"sample_id": row["sample_id"],
|
| 346 |
+
"town": row["town"],
|
| 347 |
+
"profile": row["profile"],
|
| 348 |
+
"reason": row["eligibility"],
|
| 349 |
+
}
|
| 350 |
+
for row in excluded
|
| 351 |
+
],
|
| 352 |
+
}
|
| 353 |
+
|
| 354 |
+
|
| 355 |
+
def aggregate_magnitude_stability(rows: list[dict[str, Any]]) -> dict[str, Any]:
|
| 356 |
+
baseline_pairs = sum(
|
| 357 |
+
int(row["symmetric_center_equivalent_vs_position_increment_geometry"]["pair_count"])
|
| 358 |
+
for row in rows
|
| 359 |
+
)
|
| 360 |
+
thresholds: dict[str, Any] = {}
|
| 361 |
+
for threshold in MAGNITUDE_RELATIVE_THRESHOLDS:
|
| 362 |
+
key = "nonzero" if threshold == 0.0 else f"{threshold:.0e}"
|
| 363 |
+
episode_summaries = [
|
| 364 |
+
row["center_coefficient_magnitude_stability"]["threshold_sensitivity_vs_position_increment"][key]
|
| 365 |
+
for row in rows
|
| 366 |
+
]
|
| 367 |
+
eligible = [summary for summary in episode_summaries if int(summary["pair_count"]) > 0]
|
| 368 |
+
retained_pairs = sum(int(summary["pair_count"]) for summary in eligible)
|
| 369 |
+
thresholds[key] = {
|
| 370 |
+
"relative_threshold": float(threshold),
|
| 371 |
+
"eligible_episode_count": len(eligible),
|
| 372 |
+
"pair_count": retained_pairs,
|
| 373 |
+
"retained_fraction": retained_pairs / baseline_pairs if baseline_pairs else None,
|
| 374 |
+
"episode_mean_mae_hz": (
|
| 375 |
+
float(np.mean([summary["mae_hz"] for summary in eligible])) if eligible else None
|
| 376 |
+
),
|
| 377 |
+
"episode_mean_p95_abs_hz": (
|
| 378 |
+
float(np.mean([summary["p95_abs_hz"] for summary in eligible])) if eligible else None
|
| 379 |
+
),
|
| 380 |
+
"pooled_max_abs_hz": (
|
| 381 |
+
float(np.max([summary["max_abs_hz"] for summary in eligible])) if eligible else None
|
| 382 |
+
),
|
| 383 |
+
}
|
| 384 |
+
episode_min_relative = [
|
| 385 |
+
row["center_coefficient_magnitude_stability"]["pair_min_relative_to_episode_max"]["min"]
|
| 386 |
+
for row in rows
|
| 387 |
+
if row["center_coefficient_magnitude_stability"]["pair_min_relative_to_episode_max"]["min"] is not None
|
| 388 |
+
]
|
| 389 |
+
return {
|
| 390 |
+
"reference": "minimum magnitude across the four central type-0 coefficients in both adjacent states, divided by the episode maximum across those coefficients",
|
| 391 |
+
"baseline_pair_count": baseline_pairs,
|
| 392 |
+
"minimum_episode_pair_relative_magnitude": (
|
| 393 |
+
float(np.min(episode_min_relative)) if episode_min_relative else None
|
| 394 |
+
),
|
| 395 |
+
"threshold_sensitivity": thresholds,
|
| 396 |
+
}
|
| 397 |
+
|
| 398 |
+
|
| 399 |
def plot_center_position_parity(rows: list[dict[str, Any]], destination: Path) -> None:
|
| 400 |
key = "symmetric_center_equivalent_vs_position_increment_geometry"
|
| 401 |
expected = np.asarray([row[key]["expected_mean_hz"] for row in rows], dtype=np.float64)
|
|
|
|
| 451 |
"rx_normalized_y_lambda": [-0.25, 0.25],
|
| 452 |
},
|
| 453 |
"semantic_note": "Legacy Rx0/Tx0 phase is an element-pair quantity. The symmetric product cancels array offsets and is compared with endpoint-center geometry.",
|
| 454 |
+
"eligibility_coverage": eligibility_coverage(rows),
|
| 455 |
"legacy": aggregate(rows, "legacy_rx0_tx0_vs_center_geometry"),
|
| 456 |
"center_equivalent_vs_stored_velocity": aggregate(rows, "symmetric_center_equivalent_vs_stored_velocity_geometry"),
|
| 457 |
"center_equivalent_vs_position_increment": aggregate(rows, "symmetric_center_equivalent_vs_position_increment_geometry"),
|
| 458 |
+
"center_coefficient_magnitude_stability": aggregate_magnitude_stability(rows),
|
| 459 |
"episodes": rows,
|
| 460 |
}
|
| 461 |
args.output_root.mkdir(parents=True, exist_ok=True)
|
|
|
|
| 471 |
"",
|
| 472 |
report["scope"],
|
| 473 |
"",
|
| 474 |
+
f"Selected episodes: {report['eligibility_coverage']['selected_episode_count']}; eligible: {report['eligibility_coverage']['eligible_episode_count']} across {report['eligibility_coverage']['eligible_town_count']} Towns and {report['eligibility_coverage']['eligible_profile_count']} profiles.",
|
| 475 |
+
"",
|
| 476 |
"| Definition | Adjacent pairs | Episode-mean MAE | Episode-mean bias | Episode-mean P95 abs. |",
|
| 477 |
"|---|---:|---:|---:|---:|",
|
| 478 |
f"| Legacy Rx0/Tx0 vs endpoint center | {legacy['pair_count']:,} | {legacy['episode_mean_mae_hz']:.4f} Hz | {legacy['episode_mean_bias_hz']:.4f} Hz | {legacy['episode_mean_p95_abs_hz']:.4f} Hz |",
|
|
|
|
| 482 |
f"Center-equivalent episode-mean Pearson correlation: {center_position['episode_mean_pearson_r']:.6f} against adjacent-position range rate ({center['episode_mean_pearson_r']:.6f} against stored velocity).",
|
| 483 |
f"The position-derived episode-mean fit has slope {center_position['episode_mean_linear_fit']['slope']:.8f} and intercept {center_position['episode_mean_linear_fit']['intercept_hz']:.6f} Hz; its pooled maximum absolute pair error is {center_position['pooled_max_abs_hz']:.4f} Hz.",
|
| 484 |
"",
|
| 485 |
+
"Magnitude-threshold sensitivity (threshold is relative to the episode maximum across the four central direct coefficients):",
|
| 486 |
+
"",
|
| 487 |
+
"| Relative threshold | Eligible episodes | Adjacent pairs | Retained | Episode-mean MAE | Episode-mean P95 abs. | Pooled max abs. |",
|
| 488 |
+
"|---:|---:|---:|---:|---:|---:|---:|",
|
| 489 |
+
*[
|
| 490 |
+
f"| {entry['relative_threshold']:.0e} | {entry['eligible_episode_count']} | {entry['pair_count']:,} | {entry['retained_fraction']:.6f} | {entry['episode_mean_mae_hz']:.4f} Hz | {entry['episode_mean_p95_abs_hz']:.4f} Hz | {entry['pooled_max_abs_hz']:.4f} Hz |"
|
| 491 |
+
for entry in report["center_coefficient_magnitude_stability"]["threshold_sensitivity"].values()
|
| 492 |
+
],
|
| 493 |
+
"",
|
| 494 |
"The legacy definition is retained only as a diagnostic. Paper claims should use the center-equivalent result or explicitly name the antenna element pair.",
|
| 495 |
]
|
| 496 |
(args.output_root / "README.md").write_text("\n".join(lines) + "\n", encoding="utf-8")
|