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‎notebooks/Complete_DLSFH_Dual_Overlay_Test.ipynb‎

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‎notebooks/DLSFH_Entropy_AutoAnalysis_Patched.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/geopayme/AstroPhysics/blob/main/DLSFH_Entropy_AutoAnalysis_Patched.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c5b0c5e8",
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"metadata": {
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"id": "c5b0c5e8"
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},
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"source": [
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"# ☀️ DLSFH Enhanced Diagnostic Notebook\n",
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"Includes entropy mapping, SGCV coherence analysis, and NOAA sunspot timeline with fallback logic."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "19cc100c",
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"metadata": {
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"id": "19cc100c"
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},
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"outputs": [],
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"source": [
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"# ✅ Install dependencies (Colab only)\n",
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"!apt-get install -y tesseract-ocr\n",
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"!pip install -q pytesseract\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d917bec4",
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"metadata": {
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"id": "d917bec4"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.dates as mdates\n",
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"from PIL import Image\n",
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"import pytesseract\n",
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"from datetime import datetime\n",
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"from scipy.ndimage import sobel, generic_filter\n",
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"from google.colab import files\n",
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"import io, requests\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "664dfaa1",
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"metadata": {
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"id": "664dfaa1"
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},
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"outputs": [],
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"source": [
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"# 📤 Upload solar magnetogram\n",
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"uploaded = files.upload()\n",
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"image_path = next(iter(uploaded))\n",
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"img = Image.open(image_path).convert('L')\n",
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"img_array = np.array(img)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9ea2dca5",
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"metadata": {
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"id": "9ea2dca5"
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},
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"outputs": [],
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"source": [
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"# 🧠 Extract date from filename → OCR → manual\n",
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"try:\n",
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" base = image_path.split('/')[-1]\n",
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" date_str = base[5:11] # e.g., '250328'\n",
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" year = int('20' + date_str[:2])\n",
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" month = int(date_str[2:4])\n",
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" day = int(date_str[4:6])\n",
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" image_date = datetime(year, month, day)\n",
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" print(f\"✅ Date from filename: {image_date.date()}\")\n",
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"except:\n",
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" try:\n",
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" text = pytesseract.image_to_string(img)\n",
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" date_parts = [int(p) for p in text.split()[0].split('-')]\n",
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" image_date = datetime(*date_parts)\n",
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" print(f\"✅ Date from OCR: {image_date.date()}\")\n",
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" except:\n",
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" manual = input(\"❗ Enter image date (YYYY-MM-DD): \")\n",
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" image_date = datetime.strptime(manual.strip(), \"%Y-%m-%d\")\n",
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" print(f\"✅ Using manual date: {image_date.date()}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e66e0612",
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"metadata": {
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"id": "e66e0612"
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},
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"outputs": [],
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"source": [
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"# 📈 NOAA Observed Solar Cycle (API → fallback CSV)\n",
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"obs_url = \"https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json\"\n",
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"try:\n",
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" r = requests.get(obs_url)\n",
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" r.raise_for_status()\n",
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" data = r.json()\n",
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" df = pd.DataFrame(data)\n",
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" df['date'] = pd.to_datetime(df['time-tag'], errors='coerce')\n",
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" df = df[['date', 'ssn']].dropna()\n",
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" print(f\"✅ NOAA API loaded ({len(df)} records)\")\n",
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"except:\n",
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" print(\"⚠️ NOAA API failed. Upload fallback CSV (columns: date, ssn)\")\n",
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" uploaded_csv = files.upload()\n",
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" csv_name = next(iter(uploaded_csv))\n",
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" df = pd.read_csv(csv_name)\n",
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" df['date'] = pd.to_datetime(df['date'])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1dfdc983",
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"metadata": {
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"id": "1dfdc983"
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},
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"outputs": [],
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"source": [
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"# 📊 NOAA Sunspot Timeline\n",
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"fig, ax = plt.subplots(figsize=(10, 4))\n",
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"ax.plot(df['date'], df['ssn'], label='Smoothed Sunspot Number')\n",
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"ax.axvline(image_date, color='red', linestyle='--', label=f'Image Date: {image_date.date()}')\n",
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"ax.set_title('NOAA Solar Cycle Alignment')\n",
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"ax.set_ylabel('Sunspot Number')\n",
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"ax.legend()\n",
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"ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
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"plt.grid(True)\n",
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"plt.tight_layout()\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5b0b6f8d",
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"metadata": {
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"id": "5b0b6f8d"
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},
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"outputs": [],
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"source": [
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"# 🔬 Enhanced Entropy Extraction\n",
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"h, w = img_array.shape\n",
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"cx, cy = w // 2, h // 2\n",
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"radius = min(cx, cy) - 10\n",
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"y, x = np.ogrid[:h, :w]\n",
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"mask = (x - cx)**2 + (y - cy)**2 <= radius**2\n",
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"\n",
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"gx = sobel(img_array, axis=1)\n",
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"gy = sobel(img_array, axis=0)\n",
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"grad = np.hypot(gx, gy)\n",
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"var = generic_filter(img_array, np.var, size=5)\n",
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"\n",
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"grad_n = (grad - grad.min()) / (np.ptp(grad) + 1e-8)\n",
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"var_n = (var - var.min()) / (np.ptp(var) + 1e-8)\n",
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"entropy_map = np.zeros_like(img_array, dtype=float)\n",
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"entropy_map[mask] = ((grad_n + var_n) / 2.0)[mask]\n",
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"\n",
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"# Visualize\n",
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"plt.figure(figsize=(6,6))\n",
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"plt.imshow(entropy_map, cmap='inferno')\n",
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"plt.title(f'Entropy Map ({image_date.date()})')\n",
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"plt.axis('off')\n",
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"plt.colorbar()\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c0615a74",
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"metadata": {
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"id": "c0615a74"
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},
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"outputs": [],
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"source": [
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"# 🧬 DLSFH Projection + Report\n",
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"angles = np.linspace(0, 2*np.pi, 21)[:-1]\n",
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"radii = [radius * 0.4, radius * 0.8]\n",
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"vec = []\n",
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"\n",
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"for r in radii:\n",
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" for theta in angles[:10]:\n",
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" x_pt = int(cx + r * np.cos(theta))\n",
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" y_pt = int(cy + r * np.sin(theta))\n",
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" vec.append(entropy_map[y_pt, x_pt])\n",
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"\n",
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"vec = np.array(vec)\n",
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"σ_c = 0.07\n",
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"collapsed = np.sum(vec >= σ_c)\n",
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"\n",
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"print(f\"=== DLSFH Entropy Report: {image_date.date()} ===\")\n",
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"print(f\"Mean Entropy: {vec.mean():.4f} | Std Dev: {vec.std():.4f}\")\n",
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"print(f\"Collapsed Nodes (≥ {σ_c}): {collapsed} / 20\")\n",
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"print(f\"Inner Mean: {vec[:10].mean():.4f} | Outer Mean: {vec[10:].mean():.4f}\")\n",
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"print(f\"Ring Divergence (Outer - Inner): {(vec[10:].mean() - vec[:10].mean()):.4f}\")\n",
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"if collapsed > 10:\n",
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" print('⚠️ Possible surface flux instability or decoherence.')\n"
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": [],
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"include_colab_link": true
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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