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docs/python/installation.md

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+# Python Installation
+
+The `uLib` Python package exposes the Core and Math C++ libraries via [pybind11](https://pybind11.readthedocs.io) bindings. There are two ways to install it: as an **end user** (pre-built wheel / pip) or as a **developer** (editable build from source).
+
+---
+
+## Prerequisites
+
+`uLib` depends on native C++ libraries that must be compiled. Ensure the following are available in your environment before installing:
+
+| Dependency | Minimum version | Notes |
+|---|---|---|
+| Python | 3.9 | |
+| CMake | 3.12 | |
+| pybind11 | 2.6.0 | |
+| Conan | 2.x | for Eigen3 / Boost |
+| micromamba / conda | any | recommended – provides ROOT, VTK |
+
+### Creating the `mutom` Conda/Micromamba Environment
+
+A ready-to-use environment definition is provided as `condaenv.yml` at the repository root.
+
+=== "Micromamba"
+    ```bash
+    micromamba env create -f condaenv.yml
+    micromamba activate mutom
+    ```
+
+=== "Conda"
+    ```bash
+    conda env create -f condaenv.yml
+    conda activate mutom
+    ```
+
+The environment installs CMake, Conan, ROOT, VTK, and the compiler toolchain.
+
+> **CUDA (optional)**  
+> If you want GPU-accelerated voxel filtering, you also need NVCC inside the environment:
+> ```bash
+> micromamba install cuda-nvcc -c conda-forge
+> ```
+
+---
+
+## User Installation (wheel / pip)
+
+Once the native dependencies are present in your environment, install the package with Poetry or pip:
+
+```bash
+# Activate your environment first
+micromamba activate mutom
+
+# Build and install (CUDA disabled by default)
+poetry install
+
+# Build and install with CUDA support
+USE_CUDA=ON poetry install
+```
+
+After installation the module is importable from anywhere in the environment:
+
+```python
+import uLib
+print(dir(uLib.Core))
+print(dir(uLib.Math))
+```
+
+---
+
+## Developer Installation (editable / in-tree build)
+
+For development you typically want to skip the packaging layer and work directly against the CMake build tree.
+
+### Step 1 – Install Conan dependencies
+
+```bash
+conan profile detect          # first time only
+conan install . --output-folder=build --build=missing
+```
+
+### Step 2 – Configure and build
+
+```bash
+# Standard release build
+cmake --preset conan-release
+
+# …or manually
+cmake -B build \
+      -DCMAKE_TOOLCHAIN_FILE=build/conan_toolchain.cmake \
+      -DCMAKE_BUILD_TYPE=Release \
+      -DUSE_CUDA=OFF          # set to ON when a GPU is available
+
+cmake --build build --target uLib_python -j$(nproc)
+```
+
+The shared library (`uLib_python*.so`) is written to `build/src/Python/`.
+
+### Step 3 – Make the module importable
+
+Point `PYTHONPATH` at the build output **and** the Python source directory (the latter carries the `uLib/__init__.py` that stitches sub-modules together):
+
+```bash
+export PYTHONPATH="$(pwd)/build/src/Python:$(pwd)/src/Python:$PYTHONPATH"
+python -c "import uLib; print(uLib.__version__)"
+```
+
+Or, for a one-shot check:
+
+```bash
+PYTHONPATH="build/src/Python:src/Python" python src/Python/testing/pybind_test.py
+```
+
+### Step 4 – Run the tests
+
+CMake registers the Python tests alongside the C++ ones; use `ctest` from the build directory:
+
+```bash
+cd build
+ctest --output-on-failure -R pybind
+```
+
+Individual test scripts can also be run directly once `PYTHONPATH` is set:
+
+```bash
+python src/Python/testing/core_pybind_test.py
+python src/Python/testing/math_pybind_test.py
+python src/Python/testing/math_filters_test.py
+```
+
+---
+
+## Verifying the Installation
+
+```python
+import uLib
+
+# Core module
+obj    = uLib.Core.Object()
+timer  = uLib.Core.Timer()
+timer.Start()
+elapsed = timer.StopWatch()   # float, seconds
+
+# Math module
+v3 = uLib.Math.Vector3f([1.0, 0.0, 0.0])
+print(v3[0])  # 1.0
+```