dtl-parser 1.3.1

DTL (Domain Transport Language) Parser - A type-safe data format with enum validation, autofix, and multi-format export

🧬 DTL Parser

Domain Transport Language SDK for Python

🇦🇪 Product of Dubai, Made in Emirates

---

✨ Features

• 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
• JSON/CSV Export - Convert tables to JSON or CSV format
• Programmatic Creation - Create tables and documents in code
• Type-Safe - Full type hints for IDE support

---

📦 Installation

pip install dtl-parser

---

🚀 Quick Start

Parse a DTL File

from dtl_parser import DTLParser

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

# Access tables
for table in doc.tables:
    print(f"Table: {table.name}, Rows: {len(table.rows)}")
    for row in table.rows:
        print(row)

Parse a DTL String

from dtl_parser import DTLParser

dtl_content = """
@dtlv1.0^dtWEB^pMyApp^c0^s0^w0^hash
@sec^none^0x0^none^0

USERS|id:s,name:s,role:e(admin,user,guest),status:e(active,inactive)|2|S0|W0|C0
U001|Alice|admin|active
U002|Bob|user|active
"""

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

users = doc.get_table("USERS")
for row in users.rows:
    print(f"{row['name']} is a {row['role']}")

---

✅ Validation

from dtl_parser import DTLParser

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

# Validate and get errors
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

Automatically fix common errors like typos, format issues, and row count mismatches:

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'

What Autofix Corrects

Issue	Before	After
Row count mismatch	|2| (actual: 3)	|3|
Table name case	users	USERS
Boolean values	true, yes, on	1
Boolean values	false, no, off	0
Enum typos	pendng	pending
Date format (US)	01/15/2025	2025-01-15
Date format (EU)	15.01.2025	2025-01-15
Timestamp format	2025-01-15 10:30:00	2025-01-15T10:30:00Z

---

🏭 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"
})

orders.add_row({
    "id": "ORD-002",
    "customer": "Bob",
    "status": "shipped",
    "priority": "medium",
    "total": 149.50,
    "created": "2025-01-14"
})

# Create document and add table
doc = create_document(domain="dtWEB")
doc.add_table(orders)

# Export to DTL
print(doc.to_dtl())

---

📤 Export Formats

To JSON

# Table to JSON
json_data = table.to_json()
print(json.dumps(json_data, indent=2))

# Document to JSON
doc_json = doc.to_json()

To CSV

csv_data = table.to_csv()
print(csv_data)

# With custom delimiter
tsv_data = table.to_csv(delimiter="\t")

Back to DTL

dtl_string = doc.to_dtl()
print(dtl_string)

---

📊 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)

---

🔍 Fuzzy Matching

The find_closest_match function uses Levenshtein distance to suggest corrections:

from dtl_parser import find_closest_match

options = ["pending", "processing", "shipped", "delivered"]

# Find closest match
suggestion = find_closest_match("pendng", options)
print(suggestion)  # "pending"

suggestion = find_closest_match("shiped", options)
print(suggestion)  # "shipped"

---

🏥 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

---

📜 License

MIT License - see LICENSE file.

---

👤 Author

Padam Sundar Kafle
Lead Architect, DTL & AlifZetta

---

🔗 Links

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

---

🇦🇪 Product of Dubai, Made in Emirates

Born in Dubai, Built for the World