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/radialpositions10min_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. Data Sources: List the specific files you intend to use (e.g., "Using data/contrast.csv and references/nema_standards.pdf").
3. Proposed Structure: Briefly outline the sections you will write.
4. Action: Ask the user, "Does this plan match your requirements?" and wait for their confirmation or correction.

4. Report Generation

Unless otherwise specified, always consolidate findings into a new file named docs/analysis-report.md.

Report Structure:

1. Objective: Define the goal (e.g., "Compare Method A vs. Method B").
2. Methodology: Describe the experimental setup. Explicitly cite the specific data files used from data/ and standards from references/.
3. Quantitative Results: Present data in Markdown tables. Compare distinct groups (e.g., Control vs. Variable).
4. Discussion & Interpretation:

- Explain why the results occurred using the identified physical/math models. - Justify any approximations used in the code.

1. Conclusion: Summary of the primary findings.

5. Writing Standards

• Quantify Everything: Avoid vague terms. Use "12.5% higher efficiency" rather than "better efficiency."
• Writing Style: Use a professional tone. Lean towards writing in natural language paragraphs instead of using bullet points or lists.
• Visuals: If plots are generated, reference their filenames in the report.
• Language: Write in Simplified Chinese. For specific translations, see [translations.md](references/translations.md).
• Headings:

- Do not number headings. - The title should not be a heading, so sections should use heading 1 instead of 2.

• Formulas:

- Use LaTex for isotopic notation (e.g., ^{99m}Tc). - Use LaTeX-style formulas (e.g., $E = mc^2$). - Use $$ to delimit multi-line formulas.

Examples

Example 1: General Performance Analysis

User: "Analyze the stability of the sensor data in this repo." Action:

1. Read references/sensor_datasheet.md to find the nominal operating range.
2. Load data/sensorstabilitylog_24hours.csv.
3. Calculate mean and variance.
4. Generate docs/analysis-report.md:

- Methods: "Compared observed variance in data/sensorstabilitylog24hours.csv against specs in references/sensordatasheet.md." - Results: Table showing stability metrics. - Discussion: Explain deviations based on noise models found in main.py.

Example 2: Comparative Method Study

User: "Compare the simulation results between the 'Fast' and 'Accurate' algorithms." Action:

1. Locate data/simulationoutputfastalgo.csv and data/simulationoutputaccuratealgo.csv.
2. Compare key metrics: Execution time vs. Error rate.
3. Generate docs/analysis-report.md:

- Objective: "Evaluate trade-off between speed and precision." - Results: "The 'Fast' algorithm is 10x faster but introduces a 2.3% systematic error." - Discussion: Link the error to the approximation found in the code logic.