python-recutils 0.2.0

A python implementation of recutils

python-recutils

A Python implementation of GNU recutils, a set of tools and libraries to access human-editable, text-based databases called recfiles.

Installation

pip install python-recutils

Or with uv:

uv add python-recutils

Development

To contribute or modify the library:

git clone https://github.com/hkhanna/python-recutils.git
cd python-recutils
uv sync

Run tests with:

uv run pytest tests/ -v

Publishing

1. Run uv version --bump <major, minor, patch>
2. Tag and push: git tag v0.x.x && git push --tags

Usage

Parsing Rec Files

from recutils import parse, parse_file

# Parse from string
data = """
Name: Ada Lovelace
Age: 36

Name: Peter the Great
Age: 53
"""

record_sets = parse(data)
for rs in record_sets:
    for record in rs.records:
        print(record.get_field('Name'), record.get_field('Age'))

# Parse from file
with open('contacts.rec') as f:
    record_sets = parse_file(f)

Using recsel

The recsel function mirrors the interface of the recsel command-line utility.

from recutils import recsel, format_recsel_output

data = """
%rec: Book
%mandatory: Title

Title: GNU Emacs Manual
Author: Richard M. Stallman
Location: home

Title: The Colour of Magic
Author: Terry Pratchett
Location: loaned

Title: Mio Cid
Author: Anonymous
Location: home
"""

# Select all books
result = recsel(data, record_type='Book')
print(format_recsel_output(result))

# Select with expression (like recsel -e)
result = recsel(data, record_type='Book', expression="Location = 'home'")

# Select by position (like recsel -n)
result = recsel(data, record_type='Book', indexes='0,2')

# Print specific fields (like recsel -p)
result = recsel(data, record_type='Book', print_fields='Title,Author')

# Print values only (like recsel -P)
result = recsel(data, record_type='Book', print_values='Title')
# Returns: "GNU Emacs Manual\nThe Colour of Magic\nMio Cid"

# Count records (like recsel -c)
count = recsel(data, record_type='Book', count=True)
# Returns: 3

# Sort output (like recsel -S)
result = recsel(data, record_type='Book', sort='Title')

# Random selection (like recsel -m)
result = recsel(data, record_type='Book', random_count=2)

recsel Options

Option	CLI Equivalent	Description
record_type	-t TYPE	Select records of this type
indexes	-n INDEXES	Select by position (e.g., "0,2,4-9")
expression	-e EXPR	Selection expression filter
quick	-q STR	Quick substring search
random_count	-m NUM	Select N random records
print_fields	-p FIELDS	Print fields with names
print_values	-P FIELDS	Print field values only
print_row	-R FIELDS	Print values space-separated
count	-c	Return count of matches
include_descriptors	-d	Include record descriptors
collapse	-C	Don't separate with blank lines
case_insensitive	-i	Case-insensitive matching
sort	-S FIELDS	Sort by fields
group_by	-G FIELDS	Group by fields
uniq	-U	Remove duplicate fields
join	-j FIELD	Join with records via foreign key

Aggregate Functions

Field expressions support aggregate functions that compute values across records:

from recutils import recsel

data = """
Type: EC Car
Category: Toy
Price: 12.2

Type: Terria
Category: Food
Price: 0.60

Type: Notebook
Category: Office
Price: 1.00
"""

# Count all records
result = recsel(data, print_fields="Count(Category)")
# Returns one record with Count_Category: 3

# Compute average price
result = recsel(data, print_fields="Avg(Price)")
# Returns one record with Avg_Price: 4.6

# Multiple aggregates
result = recsel(data, print_fields="Count(Category),Sum(Price),Min(Price),Max(Price)")

# Rename aggregate output with alias
result = recsel(data, print_fields="Count(Category):TotalItems")
# Returns one record with TotalItems: 3

# Per-record aggregates (when combined with regular fields)
result = recsel(data, print_fields="Type,Count(Category)")
# Returns per-record results

# Aggregates with grouping
result = recsel(data, group_by="Category", print_fields="Category,Avg(Price)")
# Returns one record per category with average price

Supported aggregate functions:

• Count(Field) - Count of values
• Avg(Field) - Average of numeric values
• Sum(Field) - Sum of numeric values
• Min(Field) - Minimum numeric value
• Max(Field) - Maximum numeric value

Joins

Join records from different record sets using foreign keys:

data = """
%rec: Person
%type: Residence rec Address

Name: Alice
Residence: home1

Name: Bob
Residence: home2

%rec: Address
%key: Id

Id: home1
Street: 123 Main St
City: Springfield

Id: home2
Street: 456 Oak Ave
City: Shelbyville
"""

# Join Person records with their Address
result = recsel(data, record_type="Person", join="Residence")
# Each Person record now includes Residence_Street, Residence_City, Residence_Id

# Combine with field selection
result = recsel(data, record_type="Person", join="Residence", print_fields="Name,Residence_City")

Field Subscript Ranges

Select specific occurrences of multi-valued fields:

data = """
Name: Mr. Foo
Email: [e-mail]
Email: [e-mail]
Email: [e-mail]
"""

# Select first email only
result = recsel(data, print_fields="Name,Email[0]")

# Select range of emails (second and third)
result = recsel(data, print_fields="Name,Email[1-2]")

Selection Expressions

Selection expressions filter records based on field values:

# Numeric comparisons
recsel(data, expression="Age < 18")
recsel(data, expression="Score >= 90")

# String equality
recsel(data, expression="Name = 'John'")
recsel(data, expression="Status != 'inactive'")

# Regex matching
recsel(data, expression=r"Email ~ '\.org$'")

# Logical operators
recsel(data, expression="Age > 18 && Status = 'active'")
recsel(data, expression="Role = 'admin' || Role = 'superuser'")
recsel(data, expression="!Disabled")

# Field count
recsel(data, expression="#Email > 1")  # Records with multiple Email fields

# Field subscripts
recsel(data, expression="Email[0] ~ 'primary'")  # First Email field

# Implies operator
recsel(data, expression="Premium => Discount")  # If Premium, must have Discount

# Ternary conditional
recsel(data, expression="Age > 18 ? 1 : 0")

# String concatenation
recsel(data, expression="First & ' ' & Last = 'John Doe'")

# Arithmetic
recsel(data, expression="Price * Quantity > 100")

Using recfix

The recfix function checks and fixes rec files, similar to the recfix command-line utility.

from recutils import recfix, format_recfix_output

data = """
%rec: Contact
%mandatory: Name Email
%type: Age int
%key: Id
%auto: Id

Name: Alice
Email: [e-mail]
Age: 30

Name: Bob
Email: [e-mail]
Age: twenty-five
"""

# Check integrity (default behavior)
result = recfix(data)
if not result.success:
    print(result.format_errors())
    # Output: error: type 'Contact' record 1 field 'Age': expected integer, got 'twenty-five'

# Sort records according to %sort specification
data_with_sort = """
%rec: Book
%sort: Title

Title: Zebra Tales
Title: Apple Picking
Title: Mountain Views
"""
result = recfix(data_with_sort, sort=True)
print(format_recfix_output(result))

# Generate auto fields for records missing them
data_with_auto = """
%rec: Item
%key: Id
%auto: Id

Name: First Item

Name: Second Item
"""
result = recfix(data_with_auto, auto=True)
# Records now have auto-generated Id fields (0, 1, ...)

# Encrypt confidential fields
data_with_confidential = """
%rec: User
%confidential: Password

Name: Alice
Password: secret123
"""
result = recfix(data_with_confidential, encrypt=True, password="mykey")

# Decrypt confidential fields
result = recfix(encrypted_data, decrypt=True, password="mykey")

# Force operations even with integrity errors
result = recfix(data, sort=True, force=True)

recfix Options

Option	CLI Equivalent	Description
check	(default)	Check database integrity
sort	-s	Sort records per %sort specification
encrypt	--encrypt	Encrypt confidential fields
decrypt	--decrypt	Decrypt confidential fields
auto	-A	Generate auto fields
password	-p	Password for encryption/decryption
force	-f	Force operations even with integrity errors

Integrity Checks

recfix validates records against their descriptor constraints:

• %mandatory: Required fields must be present
• %key: Key field must be unique across records and can only appear once per record
• %unique: Field can only appear once per record
• %singular: Field value must be unique across all records
• %prohibit: Prohibited fields must not be present
• %allowed: Only listed fields are allowed
• %type: Field values must match their declared type
• %typedef: Custom type definitions (checked for circular references and undefined types)
• %constraint: Custom constraint expressions must evaluate to true
• %size: Record set must have the specified number of records
• %confidential: Fields marked confidential must be encrypted

Supported Types

The %type directive supports these built-in types:

Type	Description	Example
int	Integer (decimal, hex with 0x, octal with leading 0)	42, 0xFF, 077
real	Floating-point number	3.14, -2.5
bool	Boolean value	yes, no, true, false, 0, 1
range MIN MAX	Integer within range	range 1 100
size N	String with max length N	size 255
line	Single-line string (no newlines)
enum VAL1 VAL2...	One of the listed values	enum draft published archived
date	Date value
email	Email address (must contain @)	[e-mail]
uuid	UUID string	550e8400-e29b-41d4-a716-446655440000
regexp /PATTERN/	String matching regex pattern	regexp /^[A-Z]{2}[0-9]{4}$/
field	Valid field name
rec TYPE	Foreign key reference to another record type	rec: Contact

Custom types can be defined with %typedef:

data = """
%rec: Person
%typedef: Percentage range 0 100
%typedef: Status enum active inactive pending
%type: Score Percentage
%type: AccountStatus Status

Name: Alice
Score: 85
AccountStatus: active
"""

Working with Records

from recutils import Record, Field

# Create a record
record = Record(fields=[
    Field('Name', 'John Doe'),
    Field('Email', '[e-mail]'),
    Field('Email', '[e-mail]'),  # Multiple fields with same name
])

# Access fields
name = record.get_field('Name')           # First value: 'John Doe'
emails = record.get_fields('Email')       # All values: ['[e-mail]', '[e-mail]']
count = record.get_field_count('Email')   # Count: 2
has_phone = record.has_field('Phone')     # False

# Convert to string (rec format)
print(str(record))
# Output:
# Name: John Doe
# Email: [e-mail]
# Email: [e-mail]

Evaluating Expressions Directly

from recutils import evaluate_sex, Record, Field

record = Record(fields=[
    Field('Age', '25'),
    Field('Status', 'active'),
])

# Evaluate expression against a record
matches = evaluate_sex("Age > 18 && Status = 'active'", record)
# Returns: True

Rec Format Overview

Recfiles are text files with a simple format:

# Comments start with #

# Record descriptor (optional, defines record type)
%rec: Contact
%mandatory: Name
%type: Age int

# Records are separated by blank lines
Name: Alice Smith
Email: [e-mail]
Age: 30

Name: Bob Jones
Email: [e-mail]
Email: [e-mail]
Age: 25
Phone: [phone]

Key concepts:

• Fields: Name: Value pairs
• Records: Groups of fields separated by blank lines
• Multi-line values: Use + continuation or \ line continuation
• Record descriptors: Special records starting with %rec: that define record types
• Comments: Lines starting with #

License

See LICENSE file for details.