🧬 DTL Parser

Domain Transport Language SDK for Python - Smart Tool Edition

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║              ⭐ Domain Transport Language - Smart Parser ⭐                  ║
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║     🇦🇪  Product of Dubai, Made in Emirates                                  ║
║         Born in Dubai, Built for the World                                   ║
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✨ Features

• 🧠 Smart Tool - Intelligent DTL processing with auto-detection
• 📊 Full DTL Parsing - Parse DTL files and strings with complete type support
• ✅ Enum Validation - Exact-match validation with up to 10 values per field
• 🔧 Autofix / Magic Corrections - Automatically fix common errors
• 🔍 Fuzzy Matching - Smart typo suggestions using Levenshtein distance
• 📤 Multi-Format Export - Convert to JSON or CSV
• 🏭 Programmatic Creation - Create tables and documents in code
• 🎨 Beautiful CLI - Colorful command-line interface
• 📝 Type-Safe - Full type hints for IDE support

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📦 Installation

pip install dtl-parser

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🚀 Quick Start

CLI Usage (Smart Tool)

# Show beautiful banner
dtl --banner

# Parse and analyze a file
dtl analyze data.dtl

# Validate with suggestions
dtl validate data.dtl

# Auto-fix errors
dtl fix data.dtl --output fixed.dtl

# Convert to JSON
dtl convert data.dtl --format json

# Pretty print
dtl parse data.dtl --pretty

Python API

from dtl_parser import DTLParser, SmartTool, print_banner

# Show the magical banner ✨
print_banner()

# Parse a DTL file
parser = DTLParser()
doc = parser.parse_file("data.dtl")

# Use the Smart Tool
tool = SmartTool()

# Analyze the file
analysis = tool.analyze("data.dtl")
print(f"Health Score: {analysis['health_score']}/100")

# Pretty print with colors
tool.pretty_print(doc)

# Health check
health = tool.health_check(doc)
print(f"Status: {health['status']}")

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🧠 Smart Tool Features

Auto Schema Inference

from dtl_parser import SmartTool

tool = SmartTool()

# Infer schema from raw data
data = [
    {"id": "U001", "name": "Alice", "status": "active", "score": 95},
    {"id": "U002", "name": "Bob", "status": "inactive", "score": 87},
    {"id": "U003", "name": "Charlie", "status": "active", "score": 92},
]

# SmartTool detects types and creates enum for 'status'
table = tool.infer_schema(data, "USERS")
print(table.to_dtl())

# Output:
# USERS|id:s,name:s,status:e(active,inactive),score:i|3|S0|W0|C0
# U001|Alice|active|95
# ...

Intelligent Query

# Query with filters
active_users = tool.query(users_table, status="active")
admins = tool.query(users_table, role="admin", status="active")

Health Check

health = tool.health_check(doc)
# Returns:
# {
#   "status": "healthy",  # healthy | warning | critical
#   "score": 95,
#   "checks": [...],
#   "recommendations": [...]
# }

---

✅ Validation

from dtl_parser import DTLParser

parser = DTLParser()
doc = parser.parse_file("data.dtl")

# Validate and get errors with suggestions
errors = doc.validate()

for error in errors:
    print(f"[{error.severity.value}] Line {error.line}: {error.message}")
    if error.suggestion:
        print(f"  💡 Suggestion: {error.suggestion}")

---

🔧 Autofix

from dtl_parser import DTLParser

dtl_with_errors = """
@dtlv1.0^dtWEB^pDemo^c0^s0^w0^hash
@sec^none^0x0^none^0

users|id:s,status:e(active,inactive,pending)|2|S0|W0|C0
U001|actve
U002|pendng
U003|inactive
"""

parser = DTLParser()
doc = parser.parse(dtl_with_errors)

# Apply autofix ✨
fixed_doc, changes = doc.autofix()

for change in changes:
    print(f"✨ {change}")

# Output:
# ✨ Fixed table name case: users → USERS
# ✨ Fixed row count in USERS: 2 → 3
# ✨ Fixed USERS[0].status: 'actve' → 'active'
# ✨ Fixed USERS[1].status: 'pendng' → 'pending'

---

🏭 Create Tables Programmatically

from dtl_parser import create_table, create_document

# Create a table with enum fields
orders = create_table("ORDERS", {
    "id": "s",
    "customer": "s",
    "status": "e(pending,confirmed,shipped,delivered)",
    "priority": "e(low,medium,high,urgent)",
    "total": "f",
    "created": "D"
})

# Add rows
orders.add_row({
    "id": "ORD-001",
    "customer": "Alice",
    "status": "pending",
    "priority": "high",
    "total": 99.99,
    "created": "2025-01-15"
})

# Create document and export
doc = create_document(domain="dtWEB")
doc.add_table(orders)
print(doc.to_dtl())

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📊 DTL Data Types

Type	Code	Description	Example
String	s	Text value	Hello World
Integer	i	Whole number	42
Float	f	Decimal number	3.14
Boolean	b	0 or 1	1
Date	D	YYYY-MM-DD	2025-01-15
Timestamp	T	ISO 8601	2025-01-15T10:30:00Z
UUID	u	Unique ID	550e8400-e29b-...
JSON	j	Embedded JSON	{"key":"value"}
Array	a(x)	List of type x	a,b,c
Enum	e(...)	Exact match	e(low,medium,high)

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🏥 Domain Examples

Healthcare (dtHC)

patients = create_table("PATIENTS", {
    "mrn": "s",
    "name": "s",
    "gender": "e(M,F,O)",
    "blood_type": "e(A+,A-,B+,B-,AB+,AB-,O+,O-)",
    "status": "e(active,discharged,deceased)"
}, security="S2")  # HIPAA compliant

Finance (dtFN)

transactions = create_table("TRANSACTIONS", {
    "id": "u",
    "type": "e(deposit,withdrawal,transfer)",
    "currency": "e(USD,EUR,GBP,AED)",
    "amount": "f",
    "status": "e(pending,completed,failed)"
}, web3="W1")  # With blockchain signature

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🎨 Beautiful Output

from dtl_parser import print_banner, Colors

# Print the banner
print_banner()       # Full banner
print_banner(mini=True)  # Compact banner

# Use colors in your output
print(f"{Colors.TEAL}DTL is awesome!{Colors.ENDC}")
print(f"{Colors.GREEN}✅ Success{Colors.ENDC}")
print(f"{Colors.RED}❌ Error{Colors.ENDC}")

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📜 License

MIT License - see LICENSE file.

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👤 Author

Padam Sundar Kafle
Lead Architect, DTL & AlifZetta
📧 padam@axz.si

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🔗 Links

• Website: dtlaz.org
• Documentation: dtlaz.org/docs
• GitHub: github.com/AlifZetta/dtl-parser-python

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🇦🇪 Product of Dubai, Made in Emirates

Born in Dubai, Built for the World

✨ Version 1.4.1 - Smart Tool Edition ✨