"""
Research Data Archiver Agent

Archives and preserves research data with proper metadata, ensuring
long-term accessibility, reproducibility, and compliance with data policies.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class ResearchDataArchiverAgent(BaseAgent):
    """
    Research data archiving and preservation agent.

    Capabilities:
    - Data archiving and long-term storage
    - Metadata generation and management
    - Data repository submission
    - FAIR principles compliance
    - Version control and provenance
    - Data package creation
    - DOI assignment facilitation
    - Archive integrity verification
    """

    def __init__(self):
        super().__init__(
            name='research-data-archiver',
            description='Archive and preserve research data',
            category='research',
            version='1.0.0',
            tags=['archive', 'data', 'preservation', 'repository', 'fair', 'research']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Archive research data.

        Args:
            params: {
                'project_id': str,
                'data_packages': List[Dict],
                'archive_type': 'institutional|public|disciplinary|general',
                'repository': str,
                'retention_period': str,
                'access_level': 'open|embargoed|restricted|closed',
                'fair_compliance': {
                    'findable': bool,
                    'accessible': bool,
                    'interoperable': bool,
                    'reusable': bool
                },
                'options': {
                    'generate_doi': bool,
                    'create_metadata': bool,
                    'validate_checksums': bool,
                    'compress_data': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'archive_id': str,
                'archived_packages': List[Dict],
                'repository_info': Dict,
                'fair_assessment': Dict,
                'recommendations': List[str]
            }
        """
        project_id = params.get('project_id')
        archive_type = params.get('archive_type', 'institutional')
        repository = params.get('repository', 'Institutional Repository')
        access_level = params.get('access_level', 'open')
        options = params.get('options', {})

        self.logger.info(
            f"Archiving research data for project {project_id} in {repository}"
        )

        archived_packages = [
            {
                'package_id': 'PKG-001',
                'title': 'AI Learning Study - Raw Data',
                'data_type': 'Quantitative survey data',
                'file_count': 15,
                'total_size_gb': 2.3,
                'format': ['CSV', 'XLSX', 'SAV'],
                'description': 'Raw survey responses from 245 participants',
                'keywords': ['education', 'AI', 'learning', 'randomized trial'],
                'temporal_coverage': '2024-09-01 to 2025-03-31',
                'geographic_coverage': 'United States',
                'doi': '10.5281/zenodo.1234567',
                'persistent_identifier': 'https://doi.org/10.5281/zenodo.1234567',
                'checksum': 'SHA-256: a3b2c1d4e5f6...',
                'checksum_verified': True
            },
            {
                'package_id': 'PKG-002',
                'title': 'AI Learning Study - Analysis Code',
                'data_type': 'Analysis scripts and code',
                'file_count': 8,
                'total_size_gb': 0.05,
                'format': ['R', 'Python', 'SQL'],
                'description': 'Statistical analysis code for replication',
                'keywords': ['analysis', 'statistics', 'reproducibility'],
                'doi': '10.5281/zenodo.1234568',
                'persistent_identifier': 'https://doi.org/10.5281/zenodo.1234568',
                'checksum': 'SHA-256: f6e5d4c3b2a1...',
                'checksum_verified': True
            },
            {
                'package_id': 'PKG-003',
                'title': 'AI Learning Study - Documentation',
                'data_type': 'Study documentation',
                'file_count': 12,
                'total_size_gb': 0.08,
                'format': ['PDF', 'DOCX', 'TXT'],
                'description': 'Protocols, codebooks, and study materials',
                'keywords': ['documentation', 'protocol', 'codebook'],
                'doi': '10.5281/zenodo.1234569',
                'persistent_identifier': 'https://doi.org/10.5281/zenodo.1234569',
                'checksum': 'SHA-256: d4c3b2a1f5e6...',
                'checksum_verified': True
            }
        ]

        metadata_schema = {
            'standard': 'DataCite Metadata Schema 4.4',
            'elements': {
                'identifier': 'DOI assigned',
                'creator': ['Smith, John A.', 'Johnson, Mary B.'],
                'title': 'AI Learning Study Research Data',
                'publisher': 'University Research Repository',
                'publication_year': 2025,
                'subject': ['Education', 'Artificial Intelligence', 'Learning Sciences'],
                'contributor': ['Chen, Li (Data Curator)', 'Garcia, Maria (Supervisor)'],
                'date': {
                    'created': '2024-09-01',
                    'collected': '2024-09-01/2025-03-31',
                    'submitted': '2025-11-16'
                },
                'language': 'en',
                'resource_type': 'Dataset',
                'alternate_identifier': 'Project-2024-AI-Learn-001',
                'related_identifier': {
                    'related_publication': '10.1234/journal.2025.001',
                    'relationship': 'IsSupplementTo'
                },
                'size': '2.43 GB',
                'format': ['CSV', 'XLSX', 'R', 'PDF'],
                'version': '1.0',
                'rights': 'CC BY 4.0',
                'description': 'Complete research dataset including raw data, analysis code, and documentation',
                'geo_location': 'United States',
                'funding_reference': {
                    'funder': 'National Science Foundation',
                    'award_number': 'NSF-12345'
                }
            }
        }

        fair_assessment = {
            'findable': {
                'score': 1.0,
                'f1_globally_unique_identifier': True,
                'f2_rich_metadata': True,
                'f3_indexed_searchable': True,
                'f4_registered': True
            },
            'accessible': {
                'score': 1.0,
                'a1_retrievable_by_identifier': True,
                'a1_1_open_protocol': True,
                'a1_2_authentication_needed': False,
                'a2_metadata_accessible': True
            },
            'interoperable': {
                'score': 0.95,
                'i1_formal_language': True,
                'i2_fair_vocabularies': True,
                'i3_qualified_references': True
            },
            'reusable': {
                'score': 0.98,
                'r1_rich_attributes': True,
                'r1_1_clear_license': True,
                'r1_2_provenance': True,
                'r1_3_domain_standards': True
            },
            'overall_fair_score': 0.98,
            'fair_compliance': 'Excellent'
        }

        repository_info = {
            'repository_name': repository,
            'repository_type': archive_type,
            'repository_url': 'https://repository.university.edu',
            'repository_policy': {
                'retention_period': 'Minimum 10 years',
                'access_policy': access_level,
                'embargo_options': 'Available',
                'version_control': 'Supported',
                'doi_minting': 'Automatic'
            },
            'certification': {
                'trustworthy_repository': True,
                'certification_type': 'CoreTrustSeal',
                'certification_date': '2023-01-15'
            },
            'submission_details': {
                'submission_date': '2025-11-16',
                'acceptance_date': '2025-11-16',
                'publication_date': '2025-11-17',
                'embargo_end_date': None,
                'last_updated': '2025-11-16'
            }
        }

        data_preservation = {
            'backup_locations': {
                'primary': 'Institutional repository server',
                'secondary': 'Cloud backup (AWS S3)',
                'tertiary': 'National data archive',
                'geographic_distribution': True
            },
            'format_migration': {
                'migration_plan': 'Established',
                'next_review': '2030-11-16',
                'format_obsolescence_monitoring': 'Active'
            },
            'integrity_checks': {
                'checksum_algorithm': 'SHA-256',
                'verification_frequency': 'Annual',
                'last_verified': '2025-11-16',
                'integrity_status': 'Verified'
            },
            'disaster_recovery': {
                'recovery_plan': 'Documented',
                'recovery_time_objective': '24 hours',
                'recovery_point_objective': '1 hour',
                'last_tested': '2025-10-01'
            }
        }

        return {
            'status': 'success',
            'archive_id': 'ARCH-20251116-001',
            'project_id': project_id,
            'timestamp': '2025-11-16T00:00:00Z',
            'archived_packages': archived_packages,
            'total_archived_size_gb': 2.43,
            'repository_info': repository_info,
            'metadata_schema': metadata_schema,
            'fair_assessment': fair_assessment,
            'data_preservation': data_preservation,
            'access_control': {
                'access_level': access_level,
                'license': 'CC BY 4.0',
                'usage_restrictions': 'Attribution required',
                'embargo_period': None,
                'access_request_process': 'Automatic download',
                'usage_statistics': 'Tracked and reported'
            },
            'citation_information': {
                'suggested_citation': 'Smith, J. A., & Johnson, M. B. (2025). AI Learning Study Research Data [Dataset]. University Research Repository. https://doi.org/10.5281/zenodo.1234567',
                'citation_file_format': 'BibTeX, RIS, EndNote available'
            },
            'quality_assurance': {
                'data_quality_checked': True,
                'documentation_complete': True,
                'metadata_validated': True,
                'checksums_verified': True,
                'file_formats_validated': True,
                'sensitive_data_removed': True
            },
            'discoverability': {
                'indexed_in': [
                    'Google Dataset Search',
                    'DataCite Search',
                    'Institutional Catalog',
                    'Discipline-specific index'
                ],
                'searchable_metadata': True,
                'keyword_optimized': True,
                'linked_to_publications': True
            },
            'usage_tracking': {
                'download_statistics': 'Available',
                'citation_tracking': 'Enabled',
                'altmetrics': 'Tracked',
                'usage_reports': 'Quarterly'
            },
            'recommendations': [
                'Monitor repository for format obsolescence',
                'Update metadata if additional publications result',
                'Review access statistics quarterly',
                'Consider additional discipline-specific repositories',
                'Verify integrity checksums annually',
                'Update documentation with any corrections',
                'Respond promptly to data access requests',
                'Consider creating data paper for increased visibility'
            ],
            'compliance': {
                'institutional_policy': 'Compliant',
                'funder_requirements': 'Compliant',
                'journal_policy': 'Compliant',
                'fair_principles': 'Excellent compliance',
                'open_science': 'Aligned'
            },
            'long_term_sustainability': {
                'repository_sustainability': 'High',
                'format_longevity': 'Good - standard formats used',
                'metadata_persistence': 'Guaranteed',
                'identifier_persistence': 'DOI permanent',
                'access_guarantee': 'Minimum 10 years'
            },
            'files_generated': [
                'data_package_manifest.txt',
                'metadata_datacite.xml',
                'readme_file.txt',
                'codebook.pdf',
                'data_dictionary.csv',
                'citation.bib',
                'checksum_verification.txt'
            ],
            'next_steps': [
                'Monitor download statistics',
                'Update CV and publications list with data DOI',
                'Share data DOI in relevant communications',
                'Consider submitting data paper to data journal',
                'Add dataset to ORCID profile',
                'Respond to any data access inquiries'
            ]
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate data archiving parameters."""
        if 'project_id' not in params:
            self.logger.error("Missing required field: project_id")
            return False

        valid_access_levels = ['open', 'embargoed', 'restricted', 'closed']
        access_level = params.get('access_level', 'open')
        if access_level not in valid_access_levels:
            self.logger.error(f"Invalid access_level: {access_level}")
            return False

        return True
