make gpr update

README.md

@@ -60,7 +60,7 @@ controls plus a pixel-identical live plot through an embedded web UI.
 
 | Path | Contents |
 | --- | --- |
-| `data_acq_and_processing/` | C++ pipeline: `sweep_orchestrator/`, `preprocessing/`, `processing/` (including `locator/`), shared `common_cpp/` (config, IPC rings), and vendored `third_party/` (Eigen, nlohmann/json). |
+| `data_acq_and_processing/` | C++ pipeline: `sweep_orchestrator/`, `preprocessing/`, `processing/` (including `locator/`), shared `common_cpp/` (config, IPC rings), the vendored `gpr_cpp/` C++/CUDA 3D SAR library, and vendored `third_party/` (Eigen, nlohmann/json). |
 | `python_app/` | Python application: `gui/`, `orchestration/` (process supervisor, shared-memory readers, config), `hardware_full/` (device drivers and services), `webui/`, `storage/`, `models/`, `workflows/`, `scripts/` (producers and tools), and `tests/`. |
 | `run_config_examples/` | Ready-made configuration profiles, one per hardware setup. |
 | `deploy/` | systemd daemon installer for Raspberry Pi appliances. |
@@ -75,12 +75,12 @@ controls plus a pixel-identical live plot through an embedded web UI.
 - A C++20 compiler and `make` — Eigen and nlohmann/json are vendored, so no extra C++ dependencies are needed.
 - **Python 3.10 or newer**, in a virtual environment.
 - For the `sar3d` processor (3D SAR): an NVIDIA GPU with the driver installed
-  (`libcuda`) and the external `gpr_cpp` library from the VNA_AlGo project. It
-  must be built **before** `make` (see below); its location is configurable via
-  the `GPR_CPP_DIR` make variable (default `$HOME/Documents/VNA_AlGo/gpr_cpp`).
-  CUDA headers/libnvrtc are taken from the pip `nvidia-*` packages in the
-  VNA_AlGo venv when present, otherwise from the system toolkit at
-  `/usr/local/cuda` (Jetson/JetPack, apt `cuda-toolkit`).
+  (`libcuda`) and `cmake` (the vendored `gpr_cpp` library at
+  `data_acq_and_processing/gpr_cpp/` builds via its own CMake, invoked
+  automatically by `make`). CUDA headers/libnvrtc are taken from the pip
+  `nvidia-*` packages in this repo's venv (installed by `requirements.txt` on
+  x86_64), otherwise from the system toolkit at `/usr/local/cuda`
+  (Jetson/JetPack, apt `cuda-toolkit`).
 
 ## Build and run
 
@@ -90,19 +90,22 @@ From the repository root:
 python3 -m venv .venv
 .venv/bin/python -m pip install --upgrade pip
 .venv/bin/python -m pip install -r requirements.txt
-# 3D SAR library (external, CMake + NVRTC; CUDA headers come from the pip
-# nvidia-* packages in the VNA_AlGo venv, or /usr/local/cuda as a fallback):
-bash ~/Documents/VNA_AlGo/gpr_cpp/build_cmake.sh
 make                     # build the C++ binaries into build/bin/
 ```
 
-To verify the sar3d processor against the VNA_AlGo reference on the recorded
+`make` also builds the vendored 3D SAR library
+(`data_acq_and_processing/gpr_cpp/`, CMake + NVRTC — needs `cmake`; CUDA
+headers come from the pip `nvidia-*` packages in `.venv`, or `/usr/local/cuda`
+as a fallback).
+
+To verify the sar3d processor against the gpr_cpp reference on the recorded
 dataset, build and run the on-demand parity tool:
 
 ```bash
 make sar3d_parity
 ./build/bin/sar3d_parity <preprocessed_dataset_dir> <center_snapshot_name>
-# compare the printed peak/argmax with gpr_cpp's ./build/standalone_bp on the same arguments
+# compare the printed peak/argmax with
+# ./data_acq_and_processing/gpr_cpp/build/standalone_bp on the same arguments
 ```
 
 Run the desktop GUI:
@@ -127,18 +130,14 @@ that pin the stock `./start.sh` provisioning works; the extra steps are only
 for the CUDA-backed `sar3d` processor:
 
 ```bash
-# 1. gpr_cpp against the JetPack system CUDA (no pip nvidia-* packages needed):
-git clone <VNA_AlGo repo> ~/Documents/VNA_AlGo
-GPR_BUILD_PYTHON=OFF bash ~/Documents/VNA_AlGo/gpr_cpp/build_cmake.sh
-# build_cmake.sh falls back to /usr/local/cuda automatically; override with
-# CUDA_INC=... CUDA_LIB=... if the toolkit lives elsewhere.
-
-# 2. copy the replay dataset (for dataset-replay profiles) and point
+# 1. copy the replay dataset (for dataset-replay profiles) and point
 #    radar.replay.dataset_dir in the profile at the local copy — the path in
 #    run_config_examples/*.json is machine-specific.
 
-# 3. normal launch; make picks up /usr/local/cuda when the VNA_AlGo venv has
-#    no pip nvidia-* packages:
+# 2. normal launch; the vendored gpr_cpp builds against the JetPack system CUDA
+#    (requirements.txt installs no pip nvidia-* packages on aarch64, and
+#    build_cmake.sh falls back to /usr/local/cuda automatically; override with
+#    CUDA_INC=... CUDA_LIB=... if the toolkit lives elsewhere):
 ./start.sh --profile run_config_examples/run_config_replay_vna.example.json
 ```