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Analyzes trash indicators in Remarkable Cloud document schemas by comparing documents that were moved to trash versus those that weren't, examining their hash changes and metadata components.

Purpose

This diagnostic function investigates how the Remarkable Cloud API represents documents that have been moved to trash. It fetches document schemas for three test cases (one trashed, two not trashed), compares their hash values before and after trash operations, and analyzes the metadata components to identify patterns or indicators that signal a document's trash status. This is useful for understanding the Remarkable Cloud sync protocol and implementing trash detection in custom sync clients.

Source Code

def analyze_trash_indicators():
    print("🗑️  Analyzing Trash Indicators in Updated Document Schemas")
    print("=" * 60)
    
    # Authenticate
    auth = RemarkableAuth()
    session = auth.get_authenticated_session()
    
    if not session:
        print("❌ Authentication failed")
        return
    
    # Test cases: compare a document that was moved to trash vs one that wasn't
    test_cases = [
        {
            'name': 'MOVED TO TRASH',
            'uuid': '206f5df3-07c2-4341-8afd-2b7362aefa91',
            'old_hash': '329fc087a9d14361688d730d66487ded228030629fd52b5d5008cc7a409ceaba',
            'new_hash': '1a653d10c6a8513136308cd573d898546b995526a076084d5ab6a00303cf57ed'
        },
        {
            'name': 'NOT MOVED (invoice vicebio)',
            'uuid': 'b47d73c5-2d7a-4e47-a293-220671e817ae', 
            'old_hash': '106c8b5e9fe2beca67dd4de6623186f68fd10befb7589104861f4554953e1a45',
            'new_hash': '106c8b5e9fe2beca67dd4de6623186f68fd10befb7589104861f4554953e1a45'  # Same
        },
        {
            'name': 'NOT MOVED (unchanged document)',
            'uuid': '39056ec1-5303-4a6b-8f04-695bdcfa8869',
            'old_hash': 'e26fe60d7a0fa768164530342f5a79612a5df14d65e7f8f891f88d83c154ccc8',
            'new_hash': 'e26fe60d7a0fa768164530342f5a79612a5df14d65e7f8f891f88d83c154ccc8'  # Same
        }
    ]
    
    for case in test_cases:
        print(f"\n📋 Analyzing: {case['name']}")
        print(f"   UUID: {case['uuid']}")
        print(f"   Hash changed: {'❌ NO' if case['old_hash'] == case['new_hash'] else '✅ YES'}")
        
        if case['old_hash'] != case['new_hash']:
            print(f"   Old hash: {case['old_hash'][:16]}...")
            print(f"   New hash: {case['new_hash'][:16]}...")
        
        try:
            # Fetch current document schema
            doc_response = session.get(f"https://eu.tectonic.remarkable.com/sync/v3/files/{case['new_hash']}")
            doc_response.raise_for_status()
            doc_content = doc_response.text
            
            print(f"   📄 DocSchema size: {len(doc_content)} bytes")
            print(f"   📄 DocSchema content:")
            
            # Display line by line
            lines = doc_content.strip().split('\n')
            for i, line in enumerate(lines):
                print(f"      Line {i}: {line}")
            
            # Parse and analyze components
            print(f"   📦 Component Analysis:")
            if len(lines) > 1:
                for line in lines[1:]:
                    if ':' in line:
                        parts = line.split(':')
                        if len(parts) >= 5:
                            comp_hash = parts[0]
                            comp_name = parts[2]
                            comp_size = parts[4]
                            
                            print(f"      🔍 Component: {comp_name}")
                            print(f"         Hash: {comp_hash[:16]}...")
                            print(f"         Size: {comp_size}")
                            
                            # Fetch component content for metadata analysis
                            if comp_name.endswith('.metadata'):
                                try:
                                    comp_response = session.get(f"https://eu.tectonic.remarkable.com/sync/v3/files/{comp_hash}")
                                    if comp_response.status_code == 200:
                                        metadata = json.loads(comp_response.text)
                                        print(f"         📝 Metadata content:")
                                        for key, value in metadata.items():
                                            print(f"            {key}: {value}")
                                except Exception as e:
                                    print(f"         ❌ Could not fetch metadata: {e}")
            
        except Exception as e:
            print(f"   ❌ Error fetching document schema: {e}")
        
        print("-" * 40)

Return Value

This function does not return any value (implicitly returns None). It outputs diagnostic information directly to stdout, including authentication status, document schema details, hash comparisons, component analysis, and metadata content for each test case.

Dependencies

• auth
• json
• requests

Required Imports

from auth import RemarkableAuth
import json

Usage Example

# Ensure auth.py module is available with RemarkableAuth class
# Run the analysis function
analyze_trash_indicators()

# Expected output:
# 🗑️  Analyzing Trash Indicators in Updated Document Schemas
# ============================================================
# 
# 📋 Analyzing: MOVED TO TRASH
#    UUID: 206f5df3-07c2-4341-8afd-2b7362aefa91
#    Hash changed: ✅ YES
#    Old hash: 329fc087a9d14361...
#    New hash: 1a653d10c6a85131...
#    📄 DocSchema size: XXX bytes
#    📄 DocSchema content:
#       Line 0: ...
#    📦 Component Analysis:
#       🔍 Component: document.metadata
#          Hash: ...
#          Size: ...
#          📝 Metadata content:
#             parent: trash
# ...

Best Practices

• This function is designed for diagnostic/testing purposes and should not be used in production code
• The hardcoded test case UUIDs and hashes are specific to a particular Remarkable Cloud account and will need to be updated for different accounts
• Ensure proper authentication is configured before running this function
• The function makes multiple API calls and may take time to complete depending on network conditions
• Error handling is basic; consider enhancing it for production use
• The function assumes the EU region endpoint (eu.tectonic.remarkable.com); adjust if using a different region
• Output is printed to stdout; consider logging or returning structured data for programmatic use

Tags

Similar Components

AI-powered semantic similarity - components with related functionality:

• function move_documents_to_trash 73.2% similar

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