MCM/A题/图像/prompt.md

TASK: Generate publication-quality, O-Prize-standard visuals for an MCM/ICM paper on smartphone battery drain. You MUST output the COMPLETE generation code for each required figure (plots + flowcharts/diagrams), plus a single “run-all” entrypoint that reproduces every figure deterministically.

CRITICAL REQUIREMENT (NON-NEGOTIABLE): PRESERVE EXISTING CONTENT INTEGRITY

• You MUST NOT rewrite, restructure, or renumber ANY existing paper sections or text.
• You MUST NOT change any existing model equations or definitions.
• Your job is ONLY to output code + figure manifest + validation checks that produce the visuals.
• Do NOT output new narrative paragraphs for the paper (captions are allowed only as a separate manifest field).

INPUTS (use only the uploaded files + user-provided data paths): A) “Required diagrams list” markdown: defines the figure lineup and intent for Fig 1–15. B) Current model/paper markdown: defines variables and equations (z, v_p, T_b, S, w; L, C, N, Ψ, T_a; power mapping; CPL closure; etc.). C) Any existing workflow/flowchart markdown (e.g., Mermaid) if provided. D) Simulation outputs / datasets: YOU MUST NOT invent filenames. Instead, you must require a user-editable config file to provide file paths.

DETERMINISM REQUIREMENTS:

• Fix all random seeds (one global seed constant).
• No dependence on system time.
• All figures must be reproducible bitwise given identical inputs.
• Use explicit figure sizes, DPI, font sizes, line widths, and layout parameters.
• Avoid any “automatic styling” that can vary by environment.

VISUAL QUALITY REQUIREMENTS (O-Prize standard):

• Export BOTH vector and raster:
  • Vector: PDF (preferred) or SVG for diagrams.
  • Raster: PNG at 300+ DPI for embedding.
• Consistent typography and sizing:
  • Use a serif family (Times-like), mathtext enabled.
  • Axis labels with units, readable ticks, uncluttered legends.
• No chartjunk: remove unnecessary spines, avoid overcrowding, use consistent margins.
• Every figure must have a clear “message” aligned with the diagram spec.

ALLOWED TOOLS / LIBS (Python):

• matplotlib (primary plotting)
• numpy, pandas
• scipy (curve fitting if needed)
• graphviz (preferred for flowcharts/diagrams via DOT)
• (Optional) matplotlib 3D for surfaces; must remain deterministic

OUTPUTS YOU MUST PRODUCE (exact order):

1. FIGURE_MANIFEST_v1 (JSON)
2. CODE_PACKAGE_v1 (multiple code files; each in its own code fence with file path header)
3. RUN_INSTRUCTIONS_v1 (plain text; exact commands)
4. VALIDATION_REPORT_v1 (JSON schema + what each check prints)

──────────────────────────────────────────────────────── METHODOLOGY (follow exactly) ────────────────────────────────────────────────────────

STEP 1 — Parse the required figure specification

• Read the “required diagrams list” markdown and extract Fig 1–15. For each figure, store:
  • fig_id (1..15)
  • title (verbatim)
  • intended location/section (if provided)
  • required chart type (flowchart, 3D surface, stacked area, heatmap, etc.)
  • required axes/annotations (e.g., ΔTTE arrow, R² > 0.99 display, “95% Confidence TTE” marker) Do NOT add or remove figures.

STEP 2 — Define a strict data contract via a user-editable config Because you cannot assume filenames, you MUST implement:

• config/figure_config.yaml This YAML must specify input paths for each figure, such as:
• ocv_samples_csv
• scenario_trajectories: mapping scenario_name -> csv_path
• mc_trajectories_path or mc_summary_csv (TTE samples)
• sensitivity_results_csv or parameter_sweep_csv
• heatmap_grid_csv (T_a, Ψ, TTE)
• aging_lifecycle_csv (cycle, SOH, TTE_full)
• radar_scores_csv (mode, metrics...) Each figure script MUST read only its declared inputs from this config.

STEP 3 — Enforce a common plotting style module (single source of truth) Create scripts/plot_style.py that:

• Sets matplotlib rcParams (font family, font size, mathtext, line width, figure dpi for save)
• Defines helper utilities:
  • save_figure(fig, out_basepath): writes .pdf and .png (png at >=300 dpi)
  • format_axes(ax): consistent grid/ticks/spines All figure scripts MUST import and use plot_style.py.

STEP 4 — Implement one script per figure (Fig 01 … Fig 15) Folder: scripts/figures/ Naming:

• fig01_macro_logic_flowchart.py
• fig02_system_interaction_diagram.py ...
• fig15_radar_user_guide.py

Each figure script MUST:

• Define a single public function: make_figure(config: dict) -> dict
• Return a dict containing:
  • output_files: list[str]
  • computed_metrics: dict (e.g., R², quantiles)
  • validation_flags: dict[str,bool]
• Save outputs under figures/ with fixed filenames: figures/Fig01.pdf, figures/Fig01.png, ... figures/Fig15.*

STEP 5 — Required figure-by-figure implementation details You MUST implement all figures listed below exactly as specified.

FIG 1 (Macro-Logic Flowchart)

• Type: diagram/flowchart
• Tool: Graphviz DOT (preferred) + python wrapper to render
• Nodes: Data Processing → Core Modeling → Application (must match spec wording)
• Output: Fig01.pdf + Fig01.png

FIG 2 (System Interaction Diagram)

• Type: diagram
• Must show inputs around system: L, C, N, G (GPS proxy), T_a; outputs: TTE, SOH
• Use Graphviz DOT with clusters: Inputs / Internal Modules / Outputs
• Output: Fig02.*

FIG 3 (OCV Curve Fitting)

• Inputs: ocv_samples_csv with columns: z, V_oc_meas
• Method: Fit to the paper’s chosen OCV form (use the exact OCV function from the paper markdown)
• Plot: scatter of data + fitted curve line
• Display: R² in the plot (must meet threshold in validation)
• Output: Fig03.*

FIG 4 (Internal Resistance Surface)

• Inputs: either (a) parameterized function R0(T_b,z or S) OR (b) a grid csv with columns (T_b, z, R0)
• Plot: 3D surface with labeled axes and units
• Output: Fig04.*

FIG 5 (Tail Energy Illustration)

• Inputs (preferred): a trajectory csv containing time, N(t), w(t), P_net(t) OR a small synthetic pulse-definition in config
• Plot: two-panel figure:
  • Top: data burst indicator (N or packet events)
  • Bottom: power state persistence (P_net or tail component k_tail*w)
• Must visually demonstrate “short burst → long tail”
• Output: Fig05.*

FIG 6 (CPL Avalanche Loop)

• Type: causal loop diagram
• Must show: V↓ → I↑ → Loss↑ → V↓↓ (exact concept)
• Use Graphviz DOT with arrow labels
• Output: Fig06.*

FIG 7 (Baseline Validation 2×2)

• Inputs: baseline trajectory csv with columns (t, z, V_term, I, T_b)
• Plot: 2×2 subplots: SOC, Voltage, Current, Temperature vs time
• Must visually confirm CPL signature: as V_term declines, I increases (include a note/annotation)
• Output: Fig07.*

FIG 8 (Power Breakdown Stacked Area)

• Inputs: baseline trajectory csv with columns (t, P_bg, P_scr, P_cpu, P_net, [P_gps if present])
• Plot: stacked area of power components vs time
• Output: Fig08.*

FIG 9 (Scenario Comparison & GPS Impact)

• Inputs: multiple scenario csvs with columns (t, z) and TTE summary values
• Plot: overlay 3–4 SOC curves (baseline, video, gaming, navigation)
• Must annotate ΔTTE due to GPS/navigation with a double-arrow and numeric label
• Output: Fig09.*

FIG 10 (Tornado Diagram)

• Inputs: sensitivity_results_csv with columns: parameter_name, low_TTE, base_TTE, high_TTE (or delta)
• Plot: horizontal tornado bars sorted by absolute impact
• Must include GPS, signal quality, temperature, brightness (as available)
• Output: Fig10.*

FIG 11 (Two-Parameter Heatmap)

• Inputs: heatmap_grid_csv with columns: T_a, Psi, TTE
• Plot: heatmap (T_a on x, Psi on y) colored by TTE (hours)
• Output: Fig11.*

FIG 12 (Monte Carlo Spaghetti Plot)

• Inputs: mc_trajectories file(s) OR a single tidy csv: run_id, t, z
• Plot: many thin SOC curves + one thick mean curve
• Use fixed seed only for any sampling/downselection
• Output: Fig12.*

FIG 13 (Reliability / Survival Curve)

• Inputs: tte_samples_csv with column: TTE
• Plot: empirical survival S(t)=P(TTE>t) as a step function
• Must mark “95% Confidence TTE” = 5th percentile (or specify explicitly) with a vertical line + label
• Output: Fig13.*

FIG 14 (Lifecycle Degradation)

• Inputs: aging_lifecycle_csv with columns: cycle_index, SOH, TTE_full
• Plot: dual-axis (SOH vs cycle, TTE_full vs cycle) with clear legends and units
• Output: Fig14.*

FIG 15 (User Guide Radar Chart)

• Inputs: radar_scores_csv with columns: mode, screen, cpu, location, network, experience (or exact metrics listed)
• Plot: radar comparing “power-saving mode” vs “high-performance mode”
• Output: Fig15.*

STEP 6 — Add a run-all pipeline Create run_all_figures.py that:

• Loads config/figure_config.yaml
• Calls each make_figure in numerical order
• Writes a single artifacts/figure_build_report.json with metrics + flags
• Exits with non-zero code if any validation fails

STEP 7 — Validation (must be implemented, not just described) Each figure must have at least one deterministic validation check:

• Fig03: R² >= 0.99 (configurable threshold but default 0.99)
• Fig07: correlation check showing CPL tendency (e.g., corr(V_term, I) < 0 under baseline)
• Fig09: ΔTTE annotation value matches computed TTE difference within tolerance
• Fig12: M >= 100 runs unless user sets otherwise
• Fig13: survival curve starts at 1 and ends near 0; 95% marker equals empirical percentile
• All plots: output files exist and are non-empty; axis labels present

──────────────────────────────────────────────────────── DELIVERABLE FORMATS (STRICT) ────────────────────────────────────────────────────────

1. FIGURE_MANIFEST_v1 (JSON only) Schema: { "global": { "seed": 12345, "output_dir": "figures", "formats": ["pdf","png"], "dpi_png": 300 }, "figures": [ { "fig_id": 1, "title": "...", "script": "scripts/figures/fig01_....py", "inputs_from_config": ["..."], "outputs": ["figures/Fig01.pdf","figures/Fig01.png"], "caption_suggestion": "..." }, ... ] }

2. CODE_PACKAGE_v1

• Output multiple code fences.
• Each code fence MUST start with a single line comment containing the file path, e.g.:

  scripts/plot_style.py

• Include at minimum:
  • config/figure_config.yaml (template with placeholders)
  • scripts/plot_style.py
  • scripts/config_io.py (loads yaml)
  • scripts/validation.py (shared checks)
  • scripts/figures/fig01_.py … scripts/figures/fig15_.py
  • run_all_figures.py
  • requirements.txt

3. RUN_INSTRUCTIONS_v1 (plain text) Must include exact commands:

• pip install -r requirements.txt
• python run_all_figures.py

4. VALIDATION_REPORT_v1 (JSON only) Schema: { "status": "PASS" or "FAIL", "failed_figures": [ ... ], "details": { "Fig03": {"R2": 0.995, "pass": true}, ... } }

FORBIDDEN:

• Do NOT modify paper text.
• Do NOT invent data. If a required input path is missing in config, raise a clear error message and stop.
• Do NOT output partial scripts; every file must be complete and runnable.
• Do NOT output any additional commentary outside the four deliverables.

NOW EXECUTE: produce the four deliverables exactly.