#!/usr/bin/env python3 """ Task 4: Light Curve Classification (AGN / SNIa / TDE / RRL / Mira) Classifies astronomical transients and variables from light curve images or text-based multi-band flux data. """ import argparse import base64 import csv import json import os import pathlib import re import time from typing import Optional from dotenv import load_dotenv load_dotenv(override=True) from openai import OpenAI # ========================= # CONFIGURATION # ========================= DATA_DIR = pathlib.Path(__file__).resolve().parent.parent.parent / "data" / "Task4_LightCurve" CLASSES = ["AGN", "SNIa", "TDE", "RRL", "Mira"] # Passband index -> LSST filter name and central wavelength (nm) PASSBAND_MAP = { 0: ("u", 365), 1: ("g", 480), 2: ("r", 620), 3: ("i", 750), 4: ("z", 870), 5: ("y", 1000), } # ========================= # CLIENT # ========================= def get_client(model: str) -> OpenAI: """Create OpenAI-compatible client based on model name. Requires environment variables: - OPENAI_API_KEY / OPENAI_BASE_URL for OpenAI/compatible models - CLAUDE_API_KEY for Claude models - GROK_API_KEY for Grok models - QWEN_API_KEY for Qwen models - INTERN_API_KEY for InternVL models """ api_key = os.getenv("OPENAI_API_KEY") base_url = os.getenv("OPENAI_BASE_URL") if "intern" in model.lower(): api_key = os.getenv("INTERN_API_KEY") base_url = os.getenv("INTERN_BASE_URL") elif "qwen" in model.lower(): api_key = os.getenv("QWEN_API_KEY") base_url = os.getenv("QWEN_BASE_URL") elif "grok" in model.lower(): api_key = os.getenv("GROK_API_KEY") elif "claude" in model.lower(): api_key = os.getenv("CLAUDE_API_KEY") return OpenAI(api_key=api_key, base_url=base_url) # ========================= # IMAGE UTILS # ========================= def encode_image(path: pathlib.Path) -> str: with open(path, "rb") as f: return base64.b64encode(f.read()).decode("utf-8") # ========================= # DATA LOADING # ========================= def load_catalog_image(data_dir: pathlib.Path = DATA_DIR): samples = [] for cls in CLASSES: fig_dir = data_dir / "figures" / cls if not fig_dir.exists(): continue for fig in sorted(fig_dir.glob("*.png")): samples.append({ "oid": fig.stem, "label": cls, "image_path": fig, }) return samples def load_catalog_text(data_dir: pathlib.Path = DATA_DIR): samples = [] for cls in CLASSES: csv_dir = data_dir / "csv" / cls if not csv_dir.exists(): continue for csv_file in sorted(csv_dir.glob("*.csv")): samples.append({ "oid": csv_file.stem, "label": cls, "csv_path": csv_file, }) return samples def format_lightcurve(csv_path: pathlib.Path) -> str: rows = [] with csv_path.open(newline="") as f: reader = csv.DictReader(f) for row in reader: rows.append(row) if not rows: return "No data available." by_band: dict[int, list] = {} for row in rows: try: pb = int(float(row["passband"])) mjd = float(row["mjd"]) flux = float(row["flux"]) flux_err = float(row["flux_err"]) detected = int(float(row.get("detected_bool", 1))) by_band.setdefault(pb, []).append((mjd, flux, flux_err, detected)) except (ValueError, KeyError): continue lines = [] for pb in sorted(by_band.keys()): band_name, wave_nm = PASSBAND_MAP.get(pb, (f"band{pb}", 0)) obs = by_band[pb] obs.sort(key=lambda x: x[0]) lines.append(f" {band_name}-band ({wave_nm} nm), {len(obs)} observations:") for mjd, flux, flux_err, detected in obs[:50]: det_str = "" if detected else " [non-detection]" lines.append(f" MJD={mjd:.4f} flux={flux:.3f} ± {flux_err:.3f}{det_str}") if len(obs) > 50: lines.append(f" ... ({len(obs) - 50} more observations)") return "\n".join(lines) # ========================= # PROMPTS # ========================= SYSTEM_PROMPT_IMAGE = """You are an expert astrophysicist classifying astronomical transients and variables based on their light curve morphology. Classify the source into exactly one of these 5 categories: **AGN** — Stochastic, red-noise variability driven by accretion disk fluctuations around a supermassive black hole. Continuous process with no defined periodicity, persisting over long epochs. **SNIa** — Thermonuclear explosion of a white dwarf. Fast rise to peak brightness followed by a characteristic exponential decline powered by radioactive decay (weeks timescale). A one-time event. **TDE** — Tidal Disruption Event: a star disrupted by a supermassive black hole. Rapid rise followed by a smooth power-law decline as stellar debris falls back. Sustained high-temperature emission. **RRL** — RR Lyrae: short-period pulsating star with a period of hours to ~1 day. Strictly periodic, sawtooth-like light curve with rapid rise and slow decline. **Mira** — Long-period pulsating AGB star. Periodic variability over months to years, with large amplitude and smooth sinusoidal-like variations. **Output requirements:** - Respond with a JSON object: {"answer": "", "reason": ""} - The "answer" field must be exactly one of: AGN, SNIa, TDE, RRL, Mira - The "reason" field should contain a brief explanation of your classification decision - Do not include any text outside the JSON object """ SYSTEM_PROMPT_IMAGE_WOGUIDE = """Classify this light curve into one of: AGN, SNIa, TDE, RRL, Mira. Output requirements: - Respond with a JSON object: {"answer": "", "reason": ""} - The "answer" field must be exactly one of: AGN, SNIa, TDE, RRL, Mira - The "reason" field should contain a brief explanation of your classification decision - Do not include any text outside the JSON object """ SYSTEM_PROMPT_TEXT = """You are an expert astrophysicist classifying astronomical transients and variables based solely on their underlying physical processes. You will be given multi-band observational flux data (flux in arbitrary units vs. Modified Julian Date). Classify the target into exactly one of the 5 categories below. Evaluate the implied timescales, periodicity, event continuity, and behavior across wavelengths. Base your answer on physical deduction alone. **AGN** — Stochastic, red-noise variability driven by accretion disk fluctuations around a supermassive black hole. Continuous process lacking any defined global periodicity, persisting over extremely long epochs. **SNIa** — Thermonuclear explosion of a white dwarf at the Chandrasekhar mass limit. Powered by radioactive decay (⁵⁶Ni → ⁵⁶Co → ⁵⁶Fe). Rapid rise to peak, then characteristic exponential decline over weeks. A single, non-repeating event. **TDE** — Tidal Disruption Event: a star disrupted by a dormant supermassive black hole. Steady fallback of stellar debris forms a temporary accretion disk. Rapid rise followed by a smooth power-law decline; sustained high-temperature emission. **RRL** — RR Lyrae: low-mass horizontal branch star with rapid radial pulsations. Strictly periodic, with period of hours to ~1 day. Sawtooth-like profile with fast rise and slow decline. **Mira** — Asymptotic Giant Branch star undergoing fundamental-mode radial pulsations. Large-amplitude, long-period variability spanning months to years. Smooth, quasi-sinusoidal variations. **Output requirements:** - Respond with a JSON object: {"answer": "", "reason": ""} - The "answer" field must be exactly one of: AGN, SNIa, TDE, RRL, Mira - The "reason" field should contain a brief explanation based on the light curve properties - Do not include any text outside the JSON object """ USER_TEXT_IMAGE = "Classify this light curve: AGN, SNIa, TDE, RRL, or Mira. Respond with JSON format." # ========================= # MODEL CALLS # ========================= def classify_image(client: OpenAI, image_path: pathlib.Path, model: str, system_prompt: str, max_completion_tokens: int): img_b64 = encode_image(image_path) messages = [ {"role": "system", "content": system_prompt}, { "role": "user", "content": [ {"type": "text", "text": USER_TEXT_IMAGE}, { "type": "image_url", "image_url": { "url": f"data:image/png;base64,{img_b64}", "detail": "high", }, }, ], }, ] extra = {"enable_thinking": False} if "qwen" in model.lower() else {} for attempt in range(5): try: response = client.chat.completions.create( model=model, messages=messages, temperature=0, max_completion_tokens=max_completion_tokens, extra_body=extra if extra else None, ) return response except Exception as e: if attempt < 4: wait = 2 ** attempt * 5 print(f" Attempt {attempt+1} failed ({e}), retrying in {wait}s...") time.sleep(wait) else: raise def classify_text(client: OpenAI, user_prompt: str, model: str, max_completion_tokens: int): messages = [ {"role": "system", "content": SYSTEM_PROMPT_TEXT}, {"role": "user", "content": user_prompt}, ] extra = {"enable_thinking": False} if "qwen" in model.lower() else {} for attempt in range(5): try: response = client.chat.completions.create( model=model, messages=messages, temperature=0, max_completion_tokens=max_completion_tokens, extra_body=extra if extra else None, ) return response except Exception as e: if attempt < 4: wait = 2 ** attempt * 5 print(f" Attempt {attempt+1} failed ({e}), retrying in {wait}s...") time.sleep(wait) else: raise # ========================= # PARSE PREDICTION # ========================= def parse_prediction(raw: str) -> dict: cleaned = re.sub(r"```json\s*", "", raw) cleaned = re.sub(r"```\s*", "", cleaned) cleaned = cleaned.strip() try: return json.loads(cleaned) except json.JSONDecodeError: return {"answer": raw, "reason": ""} def canonicalize_label(value: str) -> str: val = (value or "").strip() val_up = val.upper() if "AGN" in val_up: return "AGN" if "SNIA" in val_up or "SN IA" in val_up or "TYPE IA" in val_up or val_up == "SN": return "SNIa" if "TDE" in val_up or "TIDAL" in val_up: return "TDE" if "RRL" in val_up or "RR LYR" in val_up: return "RRL" if "MIRA" in val_up: return "Mira" return "Unknown" # ========================= # MAIN PIPELINE # ========================= def run( model: str, limit: Optional[int], results_dir: pathlib.Path, modality: str, prompt_type: str, max_completion_tokens: int, resume: bool, data_dir: pathlib.Path = DATA_DIR, ) -> pathlib.Path: client = get_client(model) if modality == "image": samples = load_catalog_image(data_dir) else: samples = load_catalog_text(data_dir) print(f"Loaded {len(samples)} samples") results_dir.mkdir(parents=True, exist_ok=True) out_path = results_dir / f"predictions-{modality}-{prompt_type}-{model}.json" results = [] processed_ids = set() if resume and out_path.exists(): with out_path.open("r") as f: results = json.load(f) processed_ids = {r.get("oid") or r.get("image") for r in results} print(f"Resuming from {len(results)} existing predictions") correct = sum(r["correct"] for r in results) total = len(results) if prompt_type == "guided": system_prompt = SYSTEM_PROMPT_IMAGE else: system_prompt = SYSTEM_PROMPT_IMAGE_WOGUIDE for i, sample in enumerate(samples): if limit is not None and i >= limit: break oid = sample["oid"] if oid in processed_ids: continue label = sample["label"] if modality == "image": response = classify_image(client, sample["image_path"], model, system_prompt, max_completion_tokens) else: lightcurve_text = format_lightcurve(sample["csv_path"]) user_prompt = f"Multi-band light curve data:\n\n{lightcurve_text}\n\nBased on the physical processes described, classify this source. Respond with JSON format." response = classify_text(client, user_prompt, model, max_completion_tokens) content = response.choices[0].message.content pred = parse_prediction(content) answer = canonicalize_label(pred.get("answer", "")) is_correct = answer == label total += 1 correct += int(is_correct) results.append({ "oid": oid, "label": label, "prediction": pred, "correct": int(is_correct), "raw_response": response.model_dump(), }) print(f"{oid}: pred={answer} label={label} {'✓' if is_correct else '✗'}") with out_path.open("w") as f: json.dump(results, f, indent=2) if total > 0: print(f"Accuracy on {total} checked: {correct}/{total} = {correct/total:.2%}") print(f"Saved predictions to {out_path}") return out_path # ========================= # ARGPARSE # ========================= def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Task4: Light Curve Classification") parser.add_argument("--model", default="gpt-4o") parser.add_argument("--modality", choices=["image", "text"], default="image") parser.add_argument("--prompt-type", choices=["guided", "woguide"], default="guided") parser.add_argument("--limit", type=int, default=None) parser.add_argument("--results-dir", type=pathlib.Path, default=pathlib.Path("./results")) parser.add_argument("--max-completion-tokens", type=int, default=16384) parser.add_argument("--resume", action="store_true") parser.add_argument("--data-dir", type=pathlib.Path, default=DATA_DIR) return parser.parse_args() if __name__ == "__main__": args = parse_args() run( model=args.model, limit=args.limit, results_dir=args.results_dir, modality=args.modality, prompt_type=args.prompt_type, max_completion_tokens=args.max_completion_tokens, resume=args.resume, data_dir=args.data_dir, )