analysis-report

# Scientific Analysis & Reporting

## Instructions

### 1. Project Exploration & Domain Mapping
Before analyzing data, map the scientific context of the repository:
- **Dependency & Logic Scan**: Check `pyproject.toml` for libraries and `main.py` (or equivalent) for the execution flow.
- **Consult References**: Check the `references/` directory for background materials, standard definitions (e.g., NEMA, IEC), or methodology specifications. Use these files to define terms and expected behaviors.
- **Identify Physical Models**: Locate the core logic defining the system (constants, equations like Inverse Square Law, statistical models).
- **Locate Data**: All experimental and simulation data is stored in `data/` with comprehensive filenames (e.g., `data/radial_positions_10min_run.csv`). Always inspect file headers to confirm units and column definitions. 
- **Locate Assets**: All assets like images or plots are stored in `assets/` with comprehensive filenames.

### 2. Data Analysis & Verification
Do not rely solely on existing summary text; verify findings by inspecting raw data or running code:
- **Execution**: If the environment allows, run analysis scripts (e.g., `uv run main.py`) to generate the most recent metrics. You are also allowed to write new Python files/scripts for analyzing data. If a package you need does not exist, you are allowed to use `uv add <package>` to add it. 
- **Extract Key Metrics**:
  - **Performance**: Efficiency, throughput, sensitivity, etc.
  - **Signal Quality**: SNR, Contrast, Resolution, etc.
  - **Statistics**: Mean, Standard Deviation, CV, etc.
- **Cross-Reference**: Compare your calculated results against theoretical expectations found in `references/`.

### 3. Goal Confirmation
**Crucial Step**: Before generating the full text of the report, pause and present a brief plan to the user to ensure alignment:
1.  **Objective**: State what you understand the primary goal to be (e.g., "I will compare the sensitivity of X vs Y").
2.  **Da