ExampleCode/example2/Cot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd7f04e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# ======================\n",
    "# 配置信息\n",
    "# ======================\n",
    "\n",
    "cot_info = {\n",
    "    \"allwise\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/allwise.json\",\n",
    "    \"galex\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/Galex.json\",\n",
    "    \"spec\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/catalogs/fastspecfit/fastspecfit_ra_dec_with_source.csv\",\n",
    "    \"vla\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/vla.json\",\n",
    "    \"vla_g\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/vla_g.json\",\n",
    "    \"chandra\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/chandra.json\",\n",
    "    \"desiimage\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/desi.json\"\n",
    "}\n",
    "\n",
    "DEFAULT_CATALOG_DIR = '/home/dell461/ljm/DATA_other/AstroM3/Dataset/catalogs/Parameters/'\n",
    "DEFAULT_SEARCH_RADIUS_ARCSEC = 2.0\n",
    "\n",
    "# ======================\n",
    "# 核心函数：交叉匹配\n",
    "# ======================\n",
    "\n",
    "def cross_match_catalogs(\n",
    "    target_ra: float,\n",
    "    target_dec: float,\n",
    "    catalog_dir: str = DEFAULT_CATALOG_DIR,\n",
    "    search_radius_arcsec: float = DEFAULT_SEARCH_RADIUS_ARCSEC,\n",
    "    ra_col_names: list = None,\n",
    "    dec_col_names: list = None,\n",
    "    verbose: bool = True\n",
    "):\n",
    "    if ra_col_names is None:\n",
    "        ra_col_names = ['ra', 'raj2000', 'ra_deg', 'ra_degrees', 'alpha']\n",
    "    if dec_col_names is None:\n",
    "        dec_col_names = ['dec', 'dej2000', 'dec_deg', 'dec_degrees', 'delta']\n",
    "\n",
    "    ra_col_names = [name.lower().strip() for name in ra_col_names]\n",
    "    dec_col_names = [name.lower().strip() for name in dec_col_names]\n",
    "\n",
    "    csv_pattern = os.path.join(catalog_dir, '*.csv') if os.path.isdir(catalog_dir) else catalog_dir\n",
    "    csv_paths = glob.glob(csv_pattern)\n",
    "\n",
    "    if not csv_paths:\n",
    "        if verbose:\n",
    "            print(f\"⚠️ No CSV files found in: {csv_pattern}\")\n",
    "        return []\n",
    "\n",
    "    all_matches = []\n",
    "    if verbose:\n",
    "        print(f\"🔍 Searching within {search_radius_arcsec} arcsec of RA={target_ra}, Dec={target_dec}\\n\")\n",
    "\n",
    "    for csv_path in sorted(csv_paths):\n",
    "        filename = os.path.basename(csv_path)\n",
    "        if verbose:\n",
    "            print(f\"Processing: {filename}\")\n",
    "\n",
    "        try:\n",
    "            df = pd.read_csv(csv_path)\n",
    "        except Exception as e:\n",
    "            if verbose:\n",
    "                print(f\"  ❌ Failed to read {filename}: {e}\")\n",
    "            continue\n",
    "\n",
    "        ra_col = dec_col = None\n",
    "        for col in df.columns:\n",
    "            col_clean = col.lower().strip()\n",
    "            if col_clean in ra_col_names:\n",
    "                ra_col = col\n",
    "            if col_clean in dec_col_names:\n",
    "                dec_col = col\n",
    "\n",
    "        if not ra_col or not dec_col:\n",
    "            if verbose:\n",
    "                print(f\"  ⚠️ RA/Dec columns not found. Skipping.\")\n",
    "            continue\n",
    "\n",
    "        df[ra_col] = pd.to_numeric(df[ra_col], errors='coerce')\n",
    "        df[dec_col] = pd.to_numeric(df[dec_col], errors='coerce')\n",
    "        df = df.dropna(subset=[ra_col, dec_col])\n",
    "\n",
    "        if df.empty:\n",
    "            if verbose:\n",
    "                print(f\"  ⚠️ No valid numeric RA/Dec rows. Skipping.\")\n",
    "            continue\n",
    "\n",
    "        dra = (df[ra_col] - target_ra) * np.cos(np.radians(target_dec))\n",
    "        ddec = df[dec_col] - target_dec\n",
    "        dist_deg = np.sqrt(dra**2 + ddec**2)\n",
    "        dist_arcsec = dist_deg * 3600.0\n",
    "\n",
    "        mask = dist_arcsec <= search_radius_arcsec\n",
    "        matched_rows = df[mask].copy()\n",
    "        matched_rows['match_distance_arcsec'] = dist_arcsec[mask].values\n",
    "\n",
    "        if not matched_rows.empty:\n",
    "            all_matches.append({\n",
    "                'file': filename,\n",
    "                'matches': matched_rows\n",
    "            })\n",
    "        else:\n",
    "            if verbose:\n",
    "                print(f\"  ❌ No match within {search_radius_arcsec} arcsec\")\n",
    "\n",
    "    if verbose:\n",
    "        print(\"\\n\" + \"=\"*60)\n",
    "        if all_matches:\n",
    "            print(f\"✅ Total matched files: {len(all_matches)}\")\n",
    "        else:\n",
    "            print(\"❌ No matches found in any file.\")\n",
    "\n",
    "    return all_matches\n",
    "\n",
    "\n",
    "# ======================\n",
    "# 加载语义描述\n",
    "# ======================\n",
    "\n",
    "def load_semantic_info(filename: str):\n",
    "    basename = filename.lower()\n",
    "    for key in cot_info:\n",
    "        if key in basename:\n",
    "            try:\n",
    "                with open(cot_info[key], 'r', encoding='utf-8') as f:\n",
    "                    return json.load(f)\n",
    "            except Exception as e:\n",
    "                print(f\"⚠️ Failed to load semantic info for {key}: {e}\")\n",
    "                return {}\n",
    "    return {}\n",
    "\n",
    "\n",
    "# ======================\n",
    "# 格式化用于 LLM 的输入\n",
    "# ======================\n",
    "\n",
    "def format_matches_for_llm(matches):\n",
    "    if not matches:\n",
    "        return \"No observational parameters found within search radius.\"\n",
    "\n",
    "    sections = []\n",
    "    for item in matches:\n",
    "        filename = item['file']\n",
    "        df = item['matches']\n",
    "        row = df.iloc[0]  # 取第一个匹配源\n",
    "\n",
    "        semantic = load_semantic_info(filename)\n",
    "        lines = [f\"[Source: {filename}]\"]\n",
    "\n",
    "        for col, val in row.items():\n",
    "            if col == 'match_distance_arcsec':\n",
    "                continue\n",
    "            if pd.isna(val) or (isinstance(val, float) and np.isnan(val)):\n",
    "                continue\n",
    "\n",
    "            if isinstance(val, float):\n",
    "                if abs(val) < 1e-4 or abs(val) > 1e5:\n",
    "                    val_str = f\"{val:.3e}\"\n",
    "                else:\n",
    "                    val_str = f\"{val:.4f}\"\n",
    "            else:\n",
    "                val_str = str(val)\n",
    "\n",
    "            desc = semantic.get(col, {}).get('description', '') if isinstance(semantic, dict) else ''\n",
    "            if desc:\n",
    "                lines.append(f\"  {col}: {val_str}  # {desc}\")\n",
    "            else:\n",
    "                lines.append(f\"  {col}: {val_str}\")\n",
    "\n",
    "        sections.append(\"\\n\".join(lines))\n",
    "\n",
    "    return \"\\n\\n\".join(sections).strip()\n",
    "\n",
    "\n",
    "# ======================\n",
    "# 调用 LLM 进行分类\n",
    "# ======================\n",
    "\n",
    "def classify_galaxy_with_llm(ra, dec, param_text):\n",
    "    prompt = f\"\"\"\n",
    "You are a professional astronomer. Analyze the following multi-wavelength observational parameters \n",
    "for a source at RA={ra}, Dec={dec}. Use the provided metadata (including units and descriptions) \n",
    "to infer the most likely galaxy type (e.g., spiral, elliptical, starburst, AGN, quasar, etc.).\n",
    "\n",
    "Observational data:\n",
    "{param_text}\n",
    "\n",
    "Provide a concise classification with reasoning based on physical indicators (e.g., X-ray luminosity, UV excess, radio morphology, IR colors), no more than 200 words.\n",
    "\"\"\"\n",
    "\n",
    "    from openai import OpenAI\n",
    "\n",
    "    OPENAI_API_KEY = \"sk-c38a5fddbcb44147b0efa9d7d1ff5aec\"\n",
    "    OPENAI_API_BASE = \"https://dashscope.aliyuncs.com/compatible-mode/v1\"\n",
    "\n",
    "    client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE)\n",
    "\n",
    "    try:\n",
    "        response = client.chat.completions.create(\n",
    "            model=\"qwen-plus\",\n",
    "            messages=[\n",
    "                {\"role\": \"system\", \"content\": \"You are a professional astronomer. Be precise, evidence-based, and concise.\"},\n",
    "                {\"role\": \"user\", \"content\": prompt.strip()}\n",
    "            ],\n",
    "            temperature=0.7,\n",
    "            max_tokens=512\n",
    "        )\n",
    "        return response.choices[0].message.content.strip()\n",
    "    except Exception as e:\n",
    "        return f\"❌ Error calling LLM: {e}\"\n",
    "\n",
    "\n",
    "# ======================\n",
    "# 下载并显示 DESI/Legacy Survey 图像\n",
    "# ======================\n",
    "\n",
    "def fetch_and_show_desi_image(ra, dec, layer=\"ls-dr9\", pixscale=0.25, size=92, save_path=\"1.jpg\"):\n",
    "    \"\"\"\n",
    "    从 Legacy Survey 下载 cutout 图像并显示。\n",
    "    \n",
    "    Parameters:\n",
    "        ra, dec: 天体坐标（度）\n",
    "        layer: 图像图层，如 'ls-dr9'\n",
    "        pixscale: 像素尺度（角秒/像素）\n",
    "        size: 图像尺寸（像素）\n",
    "        save_path: 可选，保存路径\n",
    "    \"\"\"\n",
    "    url = f\"https://www.legacysurvey.org/viewer/cutout.jpg?ra={ra}&dec={dec}&layer={layer}&pixscale={pixscale}&size={size}\"\n",
    "    \n",
    "    try:\n",
    "        response = requests.get(url)\n",
    "        response.raise_for_status()\n",
    "        img = Image.open(BytesIO(response.content))\n",
    "        \n",
    "        plt.figure(figsize=(6, 6))\n",
    "        plt.imshow(img)\n",
    "        plt.title(f\"DESI/Legacy Survey Cutout\\nRA={ra:.5f}, Dec={dec:.5f}\", fontsize=12)\n",
    "        plt.axis('off')\n",
    "\n",
    "        plt.savefig(save_path, dpi=150, bbox_inches='tight')\n",
    "\n",
    "        return img\n",
    "    except Exception as e:\n",
    "        print(f\"❌ Failed to fetch or display image: {e}\")\n",
    "        return None\n",
    "\n",
    "\n",
    "# ======================\n",
    "# 主流程函数\n",
    "# ======================\n",
    "\n",
    "def analyze_galaxy(ra: float, dec: float, show_image: bool = True, **kwargs):\n",
    "    \"\"\"\n",
    "    完整分析流程：交叉匹配 → 格式化 → LLM 分类 → 可视化图像\n",
    "    \n",
    "    Parameters:\n",
    "        ra, dec: 目标天体坐标（度）\n",
    "        show_image: 是否显示 DESI 图像\n",
    "        **kwargs: 传递给 cross_match_catalogs 的参数\n",
    "    \"\"\"\n",
    "    \n",
    "    # 1. 交叉匹配\n",
    "    matches = cross_match_catalogs(ra, dec, **kwargs)\n",
    "\n",
    "    # 2. 格式化参数\n",
    "    param_text = format_matches_for_llm(matches)\n",
    "\n",
    "    # 3. LLM 分类\n",
    "    result = classify_galaxy_with_llm(ra, dec, param_text)\n",
    "\n",
    "    fetch_and_show_desi_image(ra, dec,save_path=f\"/home/dell461/ljm/DATA_other/AstroM3/ExampleCode/example2/image/{ra}_{dec}.jpg\")\n",
    "    \n",
    "    with open(f\"/home/dell461/ljm/DATA_other/AstroM3/ExampleCode/example2/llm_result/{ra}_{dec}.txt\", \"w\") as f:\n",
    "        f.write(result)  \n",
    "\n",
    "    return {\n",
    "        \"matches\": matches,\n",
    "        \"llm_result\": result,\n",
    "        \"formatted_params\": param_text\n",
    "    }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d02a3ffd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔍 Searching within 2.0 arcsec of RA=239.5743316076613, Dec=32.91914641623415\n",
      "\n",
      "Processing: allwsie_classification_results.csv\n",
      "  ❌ No match within 2.0 arcsec\n",
      "Processing: chandraimage_classification_results.csv\n",
      "  ❌ No match within 2.0 arcsec\n",
      "Processing: desiimage_classification_results.csv\n",
      "  ❌ No match within 2.0 arcsec\n",
      "Processing: galex_classification_results.csv\n",
      "  ❌ No match within 2.0 arcsec\n",
      "Processing: vla_classification_result_g.csv\n",
      "Processing: vla_classification_results.csv\n",
      "\n",
      "============================================================\n",
      "✅ Total matched files: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ra, dec = 239.5743316076613, 32.91914641623415\n",
    "result = analyze_galaxy(ra, dec, search_radius_arcsec=2.0, verbose=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "064a599b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}