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. 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.
5. 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.
• 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/sensor_stability_log_24hours.csv

   .
3. Calculate mean and variance.
4. Generate

   docs/analysis-report.md

   :
   • Methods: "Compared observed variance in

     data/sensor_stability_log_24hours.csv

     against specs in

     references/sensor_datasheet.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/simulation_output_fast_algo.csv

   and

   data/simulation_output_accurate_algo.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.