Scientific Consistency and Framework Restructuring

xtheta-lab/notebooks/01_internal_consistency.ipynb

@@ -1,5 +1,16 @@
 {
  "cells": [
+   {
+    "cell_type": "markdown",
+    "metadata": {},
+    "source": [
+     "# 01 Internal Consistency of X-Theta Tensor Model\n",
+     "\n",
+     "This notebook verifies the internal mathematical consistency of the X-Theta correlation tensor $T(\\Phi)$.\n",
+     "\n",
+     "> **Scientific Scope:** This result is **mathematical validation**. It confirms that the core relational evolution produces the predicted tensor components $T_{xx} = T_{yy} = -\\cos(2\\Phi)$, $T_{zz} = -1$, and the anisotropy invariant $R_\\Theta = 2\\sin^2(2\\Phi)$."
+    ]
+   },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -77,6 +88,17 @@
     "plt.savefig('internal_consistency.png')\n",
     "print(\"Plots saved to internal_consistency.png\")\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "The numerical results from the engine match the analytic predictions for the correlation tensor components and the anisotropy invariant.\n",
+    "\n",
+    "**Claim Classification:** This result is **mathematical validation**. It establishes the internal consistency of the X-Theta framework's kinematic engine."
+   ]
   }
  ],
  "metadata": {

xtheta-lab/notebooks/02_benchmark_scenarios.ipynb

@@ -1,5 +1,16 @@
 {
  "cells": [
+   {
+    "cell_type": "markdown",
+    "metadata": {},
+    "source": [
+     "# 02 Benchmark Scenarios\n",
+     "\n",
+     "This notebook calculates predicted X-Theta relational phases for various physical regimes.\n",
+     "\n",
+     "> **Scientific Scope:** Micius-like and GPS-like scenarios are treated as null-regime consistency checks. The predicted X-Theta corrections are far below current experimental sensitivity and should not be interpreted as positive evidence."
+    ]
+   },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -149,6 +160,17 @@
     "    'purity'\n",
     "]])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Earth-orbit scenarios (Micius, GPS) show effectively zero predicted deviation, while extreme regimes (Neutron Star, Black Hole) show significant anisotropy.\n",
+    "\n",
+    "**Claim Classification:** This result is **simulation/benchmark**. It identifies the physical regimes where the X-Theta effect becomes potentially observable."
+   ]
   }
  ],
  "metadata": {

xtheta-lab/notebooks/03_entanglement_lensing.ipynb

@@ -11,7 +11,9 @@
     "\n",
     "We distinguish between two types of surfaces:\n",
     "1. **Direct Correlation-Strength Ellipsoid**: Shows the actual observable correlation magnitude along each axis. Radii: $r_x = r_y = |\\cos(2\\Phi)|, r_z = 1$.\n",
-    "2. **Dual Response Ellipsoid**: The inverse surface defined by $v^T(T^TT)v=1$. Radii: $r_x = r_y = 1/|\\cos(2\\Phi)|, r_z = 1$."
+    "2. **Dual Response Ellipsoid**: The inverse surface defined by $v^T(T^TT)v=1$. Radii: $r_x = r_y = 1/|\\cos(2\\Phi)|, r_z = 1$. \n",
+    "\n",
+    "> **Scientific Scope:** This notebook visualizes the tensor deformation. In the paper, this effect is called **correlation-space lensing**, distinguishing it from literal optical or gravitational lensing."
    ]
   },
   {
@@ -160,7 +162,9 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "The direct correlation-strength ellipsoid shows actual observable correlation magnitudes. The dual response ellipsoid is the inverse surface $v^T(T^TT)v=1$ and expands in directions where correlation strength weakens."
+    "The direct correlation-strength ellipsoid shows actual observable correlation magnitudes. The dual response ellipsoid is the inverse surface $v^T(T^TT)v=1$ and expands in directions where correlation strength weakens.\n",
+    "\n",
+    "**Claim Classification:** This result is **mathematical validation**. it provides a geometric interpretation of the correlation tensor anisotropy."
    ]
   }
  ],

xtheta-lab/notebooks/04_monte_carlo_uncertainty.ipynb

@@ -1,5 +1,16 @@
 {
  "cells": [
+   {
+    "cell_type": "markdown",
+    "metadata": {},
+    "source": [
+     "# 04 Monte Carlo Uncertainty Analysis\n",
+     "\n",
+     "This notebook propagates coordinate uncertainties through the X-Theta phase prediction model.\n",
+     "\n",
+     "> **Scientific Scope:** This analysis supports the null prediction for Earth-orbit scales by showing that even with coordinate uncertainties, the predicted X-Theta correction remains orders of magnitude below current CHSH sensitivity."
+    ]
+   },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -53,9 +64,20 @@
     ")\n",
     "\n",
     "print(f\"Micius Simulation Results ({res['samples']} samples):\")\n",
-    "print(f\"Phi_rel: {res['phi_mean']:.2e} +/- {res['phi_std']:.2e}\")\n",
+    "print(f\"Phi_rel: {res['Phi_mean']:.2e} +/- {res['Phi_std']:.2e}\")\n",
     "print(f\"S_max:   {res['s_max_mean']:.6f} +/- {res['s_max_std']:.2e}\")\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Monte Carlo propagation confirms that coordinate uncertainties do not bring the X-Theta effect into the measurable regime for Earth-orbit experiments.\n",
+    "\n",
+    "**Claim Classification:** This result is **simulation/benchmark**. It quantifies the robustness of the null-prediction in the satellite regime."
+   ]
   }
  ],
  "metadata": {

xtheta-lab/notebooks/05_concurrence_chsh_geometry.ipynb

@@ -15,7 +15,9 @@
     "- **Horodecki Maximum**: $S_{max} = 2\\sqrt{1 + C^2} = 2\\sqrt{1 + \\cos^2(2\\Phi)}$\n",
     "- **XY Projection**: $S_{XY} = 2\\sqrt{2}|\\cos(2\\Phi)| = 2\\sqrt{2} C$\n",
     "- **XZ Projection**: $S_{XZ} = 2\\sqrt{2}\\cos^2(\\Phi)$\n",
-    "- **Anisotropy Invariant**: $R_\\Theta = 2\\sin^2(2\\Phi) = 2(1 - C^2)$"
+    "- **Anisotropy Invariant**: $R_\\Theta = 2\\sin^2(2\\Phi) = 2(1 - C^2)$\n",
+    "\n",
+    "> **Scientific Scope:** This notebook establishes the fundamental relations between measurable invariants ($S_{max}$, $R_\\Theta$) and the relational phase $\\Phi$. These relations bridge the abstract tensor theory with experimental observables."
    ]
   },
   {
@@ -140,7 +142,9 @@
     "1. The basis-independent relation is $S_{max} = 2\\sqrt{1 + C^2}$.\n",
     "2. The anisotropy invariant is $R_\\Theta = 2(1 - C^2)$.\n",
     "\n",
-    "This demonstrates that the relational phase doesn't just rotate the measurement basis but changes the underlying entanglement structure of the state as seen in any fixed laboratory frame."
+    "This demonstrates that the relational phase doesn't just rotate the measurement basis but changes the underlying entanglement structure of the state as seen in any fixed laboratory frame.\n",
+    "\n",
+    "**Claim Classification:** This result is **mathematical validation**. It provides the core mapping from theory to measurable quantum diagnostics."
    ]
   }
  ],

xtheta-lab/notebooks/06_hensen_open_data_audit.ipynb

@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook 06: Hensen Open Data Audit\n",
+    "\n",
+    "This notebook performs a detailed audit of the Hensen et al. (2015) Delft loophole-free Bell-test dataset. \n",
+    "It verifies the loading process, filtering steps, and reproduction of the published CHSH result.\n",
+    "\n",
+    "> **Scientific Scope:** This notebook validates the open-data loading, filtering, setting-pair mapping, and CHSH reconstruction pipeline. It does not claim that the Hensen experiment measured X-Theta spacetime holonomy, because the dataset does not contain the required spacetime-path or gravitational metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import annotations\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Standardized project root addition\n",
+    "project_root = Path(os.getcwd()).parent\n",
+    "if str(project_root) not in sys.path:\n",
+    "    sys.path.append(str(project_root))\n",
+    "\n",
+    "from xtheta.data.adapters.hensen import load_hensen_dataset\n",
+    "from xtheta.data.validation import run_open_data_chsh_validation\n",
+    "from xtheta.data.bell_chsh import compute_chsh_variants\n",
+    "from xtheta.data.schema import BellEventSchema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Manual Raw Audit\n",
+    "\n",
+    "We inspect the raw file structure directly to ensure the adapter is reading the correct format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = project_root / \"data\" / \"open_bell\" / \"hensen\" / \"raw\" / \"bell_open_data.txt\"\n",
+    "\n",
+    "if not data_path.exists():\n",
+    "    print(f\"[ERROR] Raw data not found at {data_path}\")\n",
+    "    print(\"Please run: python scripts/download_open_data.py --dataset hensen\")\n",
+    "else:\n",
+    "    print(f\"Raw file path: {data_path.resolve()}\")\n",
+    "    raw_lines = data_path.read_text(encoding='utf-8').splitlines()\n",
+    "    print(f\"\\nFirst 10 raw lines:\")\n",
+    "    for line in raw_lines[:10]:\n",
+    "        print(line)\n",
+    "\n",
+    "    df_raw = pd.read_csv(data_path, header=None)\n",
+    "    print(f\"\\nRaw total row count: {len(df_raw)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Apply Hensen Adapter\n",
+    "\n",
+    "The adapter applies official filtering (Hensen et al., 2015) and mapping logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if data_path.exists():\n",
+    "    data_iterator = load_hensen_dataset(str(data_path))\n",
+    "    df = pd.concat(list(data_iterator), ignore_index=True)\n",
+    "    print(f\"Valid Bell trial count: {len(df)}\")\n",
+    "    display(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Data Distribution Audit\n",
+    "\n",
+    "Verify the distribution of settings and outcomes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if data_path.exists():\n",
+    "    schema = BellEventSchema()\n",
+    "    \n",
+    "    print(\"Unique Alice Settings:\", df[schema.alice_setting].unique())\n",
+    "    print(\"Unique Bob Settings:\", df[schema.bob_setting].unique())\n",
+    "    \n",
+    "    print(\"\\nAlice Outcome Counts:\")\n",
+    "    print(df[schema.alice_outcome].value_counts())\n",
+    "    \n",
+    "    print(\"\\nBob Outcome Counts:\")\n",
+    "    print(df[schema.bob_outcome].value_counts())\n",
+    "    \n",
+    "    print(\"\\nSetting-pair counts:\")\n",
+    "    counts = df.groupby([schema.alice_setting, schema.bob_setting]).size().reset_index(name='count')\n",
+    "    print(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Correlation and CHSH Audit\n",
+    "\n",
+    "Calculate expectations $E(a,b)$ and CHSH variants manually before running the pipeline validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if data_path.exists():\n",
+    "    df['ab'] = df[schema.alice_outcome] * df[schema.bob_outcome]\n",
+    "    \n",
+    "    # Calculate E(a,b)\n",
+    "    expectations = df.groupby([schema.alice_setting, schema.bob_setting])['ab'].mean()\n",
+    "    \n",
+    "    E00 = expectations.get((0, 0), 0.0)\n",
+    "    E01 = expectations.get((0, 1), 0.0)\n",
+    "    E10 = expectations.get((1, 0), 0.0)\n",
+    "    E11 = expectations.get((1, 1), 0.0)\n",
+    "    \n",
+    "    print(f\"E00: {E00:.4f}\")\n",
+    "    print(f\"E01: {E01:.4f}\")\n",
+    "    print(f\"E10: {E10:.4f}\")\n",
+    "    print(f\"E11: {E11:.4f}\")\n",
+    "\n",
+    "    variants = compute_chsh_variants(E00, E01, E10, E11)\n",
+    "    \n",
+    "    print(\"\\nCHSH Sign Variants:\")\n",
+    "    for k, v in variants.items():\n",
+    "        if k not in ['max_abs', 'max_abs_convention']:\n",
+    "            print(f\"  {k}: {v:.4f}\")\n",
+    "            \n",
+    "    print(f\"\\nMax Absolute CHSH: {variants['max_abs']:.4f} (Convention: {variants['max_abs_convention']})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Automated Pipeline Validation\n",
+    "\n",
+    "Verify the CHSH S-value and effective phase using the standardized pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if data_path.exists():\n",
+    "    results = run_open_data_chsh_validation(\n",
+    "        load_hensen_dataset(str(data_path)),\n",
+    "        dataset_name=\"hensen_audit\",\n",
+    "        output_dir=\"../outputs/hensen_audit\",\n",
+    "        bootstrap_samples=1000\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"\\nFinal Results:\")\n",
+    "    print(f\"S = {results['CHSH_S']:.4f} \u00b1 {results['CHSH_S_se']:.4f}\")\n",
+    "    print(f\"Phi_eff = {results['Phi_eff']:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Conclusion\n",
+    "\n",
+    "Target S (Hensen 2015): 2.42 \u00b1 0.20.  \n",
+    "Observed S: {results['CHSH_S']:.4f} \u00b1 {results['CHSH_S_se']:.4f}.  \n",
+    "Valid Trials: {results['row_count']}.  \n",
+    "\n",
+    "**Claim Classification:** This result is **open-data pipeline validation**. It confirms the framework can correctly process and interpret historical Bell-test data but does not constitute physical evidence for the X-Theta theory due to missing spacetime metadata."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
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+}