"""
Data Inventory and Acquisition Tool for Grand Unified Theory
=============================================================

Assesses existing data, identifies missing datasets, and provides
download instructions for real-world validation.
"""

import os
import json
from pathlib import Path
from datetime import datetime

class DataInventory:
    def __init__(self, base_path):
        self.base_path = Path(base_path)
        self.inventory = {
            "timestamp": datetime.now().isoformat(),
            "existing_data": {},
            "missing_data": {},
            "download_instructions": [],
            "data_quality": {}
        }

    def scan_existing_data(self):
        """Scan repository for existing datasets"""
        print("\n" + "="*70)
        print("SCANNING EXISTING DATA")
        print("="*70)

        # 1. Pan-STARRS Supernova Data (Cosmic Scale)
        ps1_path = self.base_path / "bigG" / "bigG"
        ps1_sys = ps1_path / "hlsp_ps1cosmo_panstarrs_gpc1_all_model_v1_sys-full.txt"
        ps1_lc = ps1_path / "hlsp_ps1cosmo_panstarrs_gpc1_all_model_v1_lcparam-full.txt"

        if ps1_sys.exists() and ps1_lc.exists():
            sys_size = ps1_sys.stat().st_size
            lc_size = ps1_lc.stat().st_size
            self.inventory["existing_data"]["pan_starrs"] = {
                "status": "FOUND",
                "files": {
                    "systematic_errors": str(ps1_sys),
                    "light_curve_params": str(ps1_lc)
                },
                "sizes": {
                    "systematic_errors": f"{sys_size:,} bytes",
                    "light_curve_params": f"{lc_size:,} bytes"
                },
                "quality": "EXCELLENT - 1048 supernovae with full systematics",
                "scale": "Cosmic (Gpc distances, dark energy)",
                "use": "Validate emergent cosmology, dark energy density prediction"
            }
            print("✓ Pan-STARRS Supernova Data: FOUND")
            print(f"  - {sys_size/1024/1024:.1f} MB systematic errors")
            print(f"  - {lc_size/1024:.1f} KB light curve parameters")
        else:
            self.inventory["missing_data"]["pan_starrs"] = {
                "status": "MISSING",
                "importance": "HIGH",
                "url": "https://archive.stsci.edu/hlsp/ps1cosmo"
            }
            print("✗ Pan-STARRS Supernova Data: MISSING")

        # 2. Micro-scale symbolic fit results
        micro_path = self.base_path / "micro-bot-digest" / "micro-bot-digest"
        if micro_path.exists():
            fit_files = list(micro_path.glob("*symbolic_fit*.txt"))
            gpu_files = list(micro_path.glob("gpu*_emergent_constants.txt"))

            if fit_files:
                self.inventory["existing_data"]["micro_scale"] = {
                    "status": "FOUND",
                    "files": [str(f.name) for f in fit_files[:5]],
                    "count": len(fit_files),
                    "quality": "GOOD - GPU-optimized symbolic fits",
                    "scale": "Micro (Planck scale, fundamental constants)",
                    "use": "Validate dimensional DNA operator against CODATA"
                }
                print(f"✓ Micro-scale Symbolic Fits: FOUND ({len(fit_files)} files)")

        # 3. HDGL High-Precision Data
        hdgl_path = self.base_path / "hdgl_harmonics_spiral10000_analog_v30"
        hdgl_json = hdgl_path / "hdgl_spiral10000_v30.json"

        if hdgl_json.exists():
            self.inventory["existing_data"]["hdgl_precision"] = {
                "status": "FOUND",
                "file": str(hdgl_json),
                "size": f"{hdgl_json.stat().st_size:,} bytes",
                "quality": "EXCELLENT - 10,000 point spiral with analog precision",
                "scale": "Multi-scale (geometric recursion)",
                "use": "High-precision validation of φ-recursive structure"
            }
            print("✓ HDGL High-Precision Data: FOUND")

        # 4. Check for LIGO data availability
        print("\n✗ LIGO Gravitational Wave Data: NOT LOCAL")
        self.inventory["missing_data"]["ligo_gw"] = {
            "status": "MUST DOWNLOAD",
            "importance": "CRITICAL",
            "reason": "Test φ-echo prediction (3.44% amplitude)",
            "events": [
                "GW150914", "GW151226", "GW170104", "GW170608",
                "GW170814", "GW170817", "GW190412", "GW190814"
            ]
        }

    def identify_missing_data(self):
        """Identify critical missing datasets"""
        print("\n" + "="*70)
        print("IDENTIFYING MISSING DATA")
        print("="*70)

        missing = []

        # 1. LIGO Open Science Data
        missing.append({
            "name": "LIGO Gravitational Waves",
            "source": "GWOSC (Gravitational Wave Open Science Center)",
            "url": "https://gwosc.org",
            "install": "pip install gwosc",
            "importance": "CRITICAL",
            "reason": "Test unique φ-echo prediction (3.44% vs GR's 0%)",
            "events_needed": [
                "GW150914 (first detection, M=65 Msol)",
                "GW170817 (neutron star merger)",
                "GW190412 (asymmetric mass ratio)",
                "GW190814 (mystery object)"
            ],
            "data_size": "~50 MB per event (4096 Hz), ~200 MB (16384 Hz)",
            "download_method": "Python GWOSC API (automatic)",
            "validation_target": "QNM spectrum φ-ratios, echo amplitude 3.44%"
        })

        # 2. CODATA Fundamental Constants (2018/2022)
        missing.append({
            "name": "CODATA 2022 Fundamental Constants",
            "source": "NIST CODATA",
            "url": "https://physics.nist.gov/cuu/Constants/",
            "importance": "HIGH",
            "reason": "Latest precision values for micro-scale validation",
            "constants_needed": [
                "Planck constant (h)",
                "Speed of light (c)",
                "Gravitational constant (G)",
                "Elementary charge (e)",
                "Electron mass (m_e)",
                "Fine structure constant (α)",
                "Rydberg constant (R_∞)"
            ],
            "data_size": "~1 KB JSON",
            "download_method": "Direct HTTP or hardcode latest values",
            "validation_target": "Dimensional DNA predictions < 0.1% error"
        })

        # 3. Planck CMB Power Spectrum
        missing.append({
            "name": "Planck CMB Power Spectrum",
            "source": "ESA Planck Legacy Archive",
            "url": "https://pla.esac.esa.int",
            "importance": "MEDIUM",
            "reason": "Validate dark energy density at recombination",
            "files_needed": [
                "COM_PowerSpect_CMB-TT-full_R3.01.txt"
            ],
            "data_size": "~100 KB",
            "download_method": "Direct download from PLA",
            "validation_target": "Dark energy equation of state w = -1.03±0.03"
        })

        # 4. SDSS Galaxy Redshift Survey (optional, large)
        missing.append({
            "name": "SDSS Galaxy Redshifts",
            "source": "Sloan Digital Sky Survey",
            "url": "https://www.sdss.org/dr17/",
            "importance": "LOW (optional)",
            "reason": "Large-scale structure validation",
            "data_size": "~GB scale (can use summary statistics)",
            "download_method": "SDSS SQL query or summary tables",
            "validation_target": "φ-recursive structure in galaxy distribution"
        })

        for item in missing:
            self.inventory["missing_data"][item["name"]] = item
            print(f"\n{item['importance']:8} - {item['name']}")
            print(f"           Source: {item['source']}")
            print(f"           URL: {item['url']}")
            if "install" in item:
                print(f"           Install: {item['install']}")

        return missing

    def generate_download_script(self):
        """Generate automated download script"""
        print("\n" + "="*70)
        print("GENERATING DOWNLOAD SCRIPTS")
        print("="*70)

        script = """#!/usr/bin/env python3
# -*- coding: utf-8 -*-
\"\"\"
Automated Data Acquisition for Grand Unified Theory Testing
===========================================================
\"\"\"

import os
import sys
import urllib.request
import json

def download_ligo_data():
    \"\"\"Download LIGO data using GWOSC API\"\"\"
    print("\\n" + "="*70)
    print("DOWNLOADING LIGO DATA")
    print("="*70)

    try:
        from gwosc.datasets import event_gps
        from gwosc import TimeSeries
        print("OK: GWOSC installed")
    except ImportError:
        print("ERROR: GWOSC not installed")
        print("  Installing: pip install gwosc")
        os.system(f"{sys.executable} -m pip install gwosc")
        from gwosc.datasets import event_gps
        from gwosc import TimeSeries    events = ["GW150914", "GW151226", "GW170104", "GW170814", "GW190412"]

    os.makedirs("ligo_data", exist_ok=True)

    for event in events:
        print(f"\\nDownloading {event}...")
        try:
            gps = event_gps(event)

            # Download H1 (Hanford) data
            ts_H1 = TimeSeries.fetch_open_data(
                'H1', gps-32, gps+32, sample_rate=4096, cache=True
            )

            # Download L1 (Livingston) data
            ts_L1 = TimeSeries.fetch_open_data(
                'L1', gps-32, gps+32, sample_rate=4096, cache=True
            )

            # Save to numpy files
            import numpy as np
            np.savez(
                f"ligo_data/{event}.npz",
                H1_data=ts_H1.value,
                L1_data=ts_L1.value,
                times=ts_H1.times.value,
                gps_time=gps,
                sample_rate=4096
            )


            print(f"  OK: Saved {event}.npz")

        except Exception as e:
            print(f"  ERROR downloading {event}: {e}")

    print("\\nOK: LIGO data download complete")

def download_codata():
    \"\"\"Download/hardcode CODATA 2022 constants\"\"\"
    print("\\n" + "="*70)
    print("CODATA 2022 CONSTANTS")
    print("="*70)    # CODATA 2022 recommended values
    codata = {
        "speed_of_light_c": {
            "value": 299792458,
            "unit": "m/s",
            "uncertainty": 0,
            "relative_uncertainty": 0
        },
        "planck_constant_h": {
            "value": 6.62607015e-34,
            "unit": "J·s",
            "uncertainty": 0,
            "relative_uncertainty": 0
        },
        "gravitational_constant_G": {
            "value": 6.67430e-11,
            "unit": "m³/(kg·s²)",
            "uncertainty": 1.5e-15,
            "relative_uncertainty": 2.2e-5
        },
        "elementary_charge_e": {
            "value": 1.602176634e-19,
            "unit": "C",
            "uncertainty": 0,
            "relative_uncertainty": 0
        },
        "electron_mass_m_e": {
            "value": 9.1093837015e-31,
            "unit": "kg",
            "uncertainty": 2.8e-40,
            "relative_uncertainty": 3.0e-10
        },
        "fine_structure_alpha": {
            "value": 7.2973525693e-3,
            "unit": "dimensionless",
            "uncertainty": 1.1e-12,
            "relative_uncertainty": 1.5e-10
        },
        "rydberg_constant_R_inf": {
            "value": 10973731.568160,
            "unit": "m⁻¹",
            "uncertainty": 0.000021,
            "relative_uncertainty": 1.9e-12
        },
        "boltzmann_constant_k": {
            "value": 1.380649e-23,
            "unit": "J/K",
            "uncertainty": 0,
            "relative_uncertainty": 0
        }
    }


    with open("codata_2022.json", "w") as f:
        json.dump(codata, f, indent=2)

    print("OK: Saved codata_2022.json")

    for key, val in codata.items():
        print(f"  {key}: {val['value']:.6e} +/- {val['uncertainty']:.2e} {val['unit']}")

def check_existing_data():
    \"\"\"Check what we already have\"\"\"
    print("\\n" + "="*70)
    print("CHECKING EXISTING DATA")
    print("="*70)

    paths = {
        "Pan-STARRS": "bigG/bigG/hlsp_ps1cosmo_panstarrs_gpc1_all_model_v1_sys-full.txt",
        "Micro-fits": "micro-bot-digest/micro-bot-digest/",
        "HDGL JSON": "hdgl_harmonics_spiral10000_analog_v30/hdgl_spiral10000_v30.json"
    }

    for name, path in paths.items():
        if os.path.exists(path):
            if os.path.isfile(path):
                size = os.path.getsize(path)
                print(f"OK: {name:15} - {size/1024:.1f} KB")
            else:
                count = len([f for f in os.listdir(path) if f.endswith('.txt')])
                print(f"OK: {name:15} - {count} files")
        else:
            print(f"MISSING: {name:15} - NOT FOUND")if __name__ == "__main__":
    print("\\n" + "="*70)
    print("GRAND UNIFIED THEORY - DATA ACQUISITION")
    print("="*70)

    check_existing_data()
    download_codata()

    print("\\n" + "="*70)
    print("READY TO DOWNLOAD LIGO DATA")
    print("="*70)
    print("\\nThis will download ~300 MB of gravitational wave data.")
    response = input("Continue? (y/n): ")

    if response.lower() == 'y':
        download_ligo_data()
    else:
        print("\\nSkipped LIGO download. Run manually when ready:")
        print("  python download_data.py")
"""


        with open(self.base_path / "download_data.py", "w", encoding='utf-8') as f:
            f.write(script)

        print("OK: Created download_data.py")

        return script

    def save_inventory(self):
        """Save inventory to JSON"""
        output_path = self.base_path / "data_inventory.json"
        with open(output_path, "w", encoding='utf-8') as f:
            json.dump(self.inventory, f, indent=2)
        print(f"\nOK: Saved inventory to {output_path}")

    def generate_report(self):
        """Generate human-readable report"""
        report = []
        report.append("="*70)
        report.append("DATA INVENTORY REPORT")
        report.append("="*70)
        report.append(f"Generated: {self.inventory['timestamp']}")
        report.append("")

        report.append("EXISTING DATA (READY TO USE)")
        report.append("-"*70)
        for name, info in self.inventory["existing_data"].items():
            report.append(f"\n{name.upper().replace('_', ' ')}")
            report.append(f"  Status: {info['status']}")
            report.append(f"  Quality: {info['quality']}")
            report.append(f"  Scale: {info['scale']}")
            report.append(f"  Use: {info['use']}")

        report.append("\n" + "="*70)
        report.append("MISSING DATA (MUST ACQUIRE)")
        report.append("="*70)
        for name, info in self.inventory["missing_data"].items():
            if isinstance(info, dict) and "importance" in info:
                report.append(f"\n{name}")
                report.append(f"  Priority: {info['importance']}")
                report.append(f"  Source: {info.get('source', 'N/A')}")
                report.append(f"  URL: {info.get('url', 'N/A')}")
                if "install" in info:
                    report.append(f"  Install: {info['install']}")

        report.append("\n" + "="*70)
        report.append("NEXT STEPS")
        report.append("="*70)
        report.append("1. Run: python download_data.py")
        report.append("2. Install GWOSC: pip install gwosc")
        report.append("3. Verify data: python gut_data_analysis.py --check")
        report.append("4. Build Docker: docker build -t gut-testing .")
        report.append("5. Run tests: docker run gut-testing")

        report_text = "\n".join(report)

        with open(self.base_path / "DATA_REPORT.txt", "w", encoding='utf-8') as f:
            f.write(report_text)

        print("\n" + report_text)

if __name__ == "__main__":
    base = Path(__file__).parent
    inventory = DataInventory(base)

    inventory.scan_existing_data()
    inventory.identify_missing_data()
    inventory.generate_download_script()
    inventory.save_inventory()
    inventory.generate_report()

    print("\n" + "="*70)
    print("INVENTORY COMPLETE")
    print("="*70)
    print("OK: data_inventory.json - Machine-readable inventory")
    print("OK: DATA_REPORT.txt - Human-readable report")
    print("OK: download_data.py - Automated download script")
