POPxf Parser

A lightweight Python parser and validator for POPxf (Polynomial Parameterization eXchange Format) files. POPxf is a JSON-based format for storing predictions as polynomial expansions in parameters.

Features

Schema Validation: Validates POPxf JSON files against versioned JSON schemas
Array Length Validation: Verifies consistency between data arrays and metadata specifications
Type-Safe Polynomials: Stores polynomial data in validated Python objects with numpy arrays
Comprehensive Error Messages: Provides detailed, actionable error messages for validation failures

Installation

# Clone the repository
git clone https://github.com/pop-xf/parser.git
cd parser

# Install dependencies
pip install numpy jsonschema

Architecture

The package provides two main classes with different use cases:

POPxfValidator

Purpose: Schema validation and metadata access
Use when: You only need to validate file structure and access metadata
Advantage: Faster, lower memory footprint (doesn't create polynomial objects)
Example use case: Batch validation of files, quick metadata extraction

POPxfParser

Purpose: Full parsing with polynomial objects (extends POPxfValidator)
Use when: You need to work with polynomial coefficients programmatically
Advantage: Type-safe polynomial access with numpy arrays
Example use case: Computing predictions, analyzing coefficient structure

Quick Start

Validation Only

import json
from validator import POPxfValidator

with open('examples/BR_B0_mumu.json') as f:
    validator = POPxfValidator(json.load(f))

print(validator.info())  # Display summary
print(f"Mode: {validator.mode}")
print(f"Parameters: {validator.parameters}")

Full Parsing

import json
from parser import POPxfParser

with open('examples/BR_B0_mumu.json') as f:
    parser = POPxfParser(json.load(f))

# Access polynomial data
poly = parser.observable_central
print(f"Polynomial has {len(poly)} terms")
print(f"Parameters: {poly.parameters}")

Command-Line Tool

The package includes a command-line tool validate-popxf for validating and inspecting POPxf files.

Basic Usage

# Validate a POPxf file
python bin/validate-popxf examples/BR_B0_mumu.json

# Validate with detailed information
python bin/validate-popxf examples/BR_B0_mumu.json --verbose

# Quiet mode (only show errors)
python bin/validate-popxf examples/BR_B0_mumu.json --quiet

# Display polynomial coefficients
python bin/validate-popxf examples/BR_B0_mumu.json --show-data

# Show detailed uncertainty information
python bin/validate-popxf examples/BR_B0_mumu.json --show-uncertainties

# Combine options
python bin/validate-popxf examples/BR_B0_mumu.json --verbose --show-data --show-uncertainties

Command-Line Options

Option	Description
`input_file`	Path to the POPxf JSON file to parse (required)
`-v`, `--verbose`	Print detailed information including reproducibility info and observable expressions
`-q`, `--quiet`	Suppress output (only show validation status or errors)
`--show-data`	Display full polynomial coefficient data
`--show-uncertainties`	Show detailed uncertainty information
`-h`, `--help`	Display help message and usage examples

Example Output

$ python bin/validate-popxf examples/BR_B0_mumu.json

======================================================================
Reading JSON file: examples/BR_B0_mumu.json
======================================================================
✓ examples/BR_B0_mumu.json is valid
======================================================================
POPxfParser Object Properties
======================================================================

Schema version: popxf-1.0.json

Mode: Single-Polynomial (SP)
Polynomial Degree: 2
Length (number of observables): 1
Observable Names: ['BR(B0->mumu)']
Parameters: ['C10_bdmumu', 'C10p_bdmumu']
Scale: 4.8 [GeV]

======================================================================
No uncertainty information available.
======================================================================

Error Handling

The tool provides detailed error messages for various failure scenarios:

# Missing file
$ python bin/validate-popxf nonexistent.json

======================================================================
Reading JSON file: nonexistent.json
======================================================================
POPxfIOError
======================================================================

File not found: nonexistent.json

======================================================================

# Invalid JSON
$ python bin/validate-popxf invalid.json

======================================================================
Reading JSON file: invalid.json
======================================================================
POPxfJSONError
======================================================================

Invalid JSON format in file: invalid.json
  Line 1, Column 1: Expecting value

======================================================================

# Schema validation error
$ python bin/validate-popxf examples/bad/missing_schema.json

======================================================================
Reading JSON file: examples/bad/missing_schema.json
======================================================================
POPxfValidationError
======================================================================

POPxf JSON data is missing required '$schema' field.

======================================================================

Exit Codes

0: Validation successful
1: Error occurred (file not found, invalid JSON, validation failure, etc.)

POPxf Modes

Single-Polynomial (SP) Mode

In SP mode, observable predictions are directly represented as polynomials in parameters

Required fields:

data.observable_central: Polynomial coefficients for central values

Example:

# Observable is a direct polynomial in parameters
observable = c0 + c1*C1 + c2*C2 + c3*C1*C2 + ...

Function-Of-Polynomials (FOP) Mode

In FOP mode, observables are computed as functions of auxiliary polynomials.

Required fields:

metadata.polynomial_names: Names of auxiliary polynomials
metadata.observable_expressions: Expressions relating observables to polynomials
data.polynomial_central: Auxiliary polynomial coefficients

Example:

# Observables computed from auxiliary polynomials
poly1 = p0 + p1*C1 + p2*C2 + ...
poly2 = q0 + q1*C1 + q2*C2 + ...
observable = f(poly1, poly2)  # e.g., poly1/poly2

Core Classes

POPxfValidator

Base validator class that performs schema validation and mode detection without parsing polynomial data.

Attributes:

mode: Operating mode ('SP' or 'FOP')
schema_version: POPxf schema version
polynomial_degree: Degree of polynomial expansion (default: 2)
parameters: List of parameters
metadata: Full metadata dictionary
data: Raw data dictionary

Methods:

validate_schema(): Validate JSON against POPxf schema
get_mode(): Determine SP or FOP mode
validate_scale(): Validate scale field consistency
info(): Generate formatted summary string

Use Case: When you only need validation without the overhead of parsing polynomial objects.

from validator import POPxfValidator

validator = POPxfValidator(json_data)
print(validator.info())  # Summary without polynomial parsing

POPxfParser

Parser class that extends POPxfValidator to parse polynomial data into Python objects.

Attributes:

All attributes from POPxfValidator, plus:
observable_central: Central value polynomial (SP mode, POPxfPolynomial)
polynomial_central: Auxiliary polynomials (FOP mode, POPxfPolynomial)
observable_uncertainties: Dictionary of uncertainty polynomials (POPxfPolynomialUncertainty)

Methods:

All methods from POPxfValidator, plus:
get_poly_data(): Parse and validate polynomial data

Use Case: When you need to work with polynomial data programmatically.

from parser import POPxfParser

parser = POPxfParser(json_data)
poly = parser.observable_central  # Access parsed polynomial
print(poly.parameters)  # ('C1', 'C2', ...)

POPxfPolynomial

Container for polynomial data with validated keys and values.

Key Features:

Keys are tuples of parameter names (must be alphabetically ordered)
Values are 1D lists or numpy arrays of coefficients
Supports real/imaginary component specifications
Automatic parameter extraction

Example:

poly_data = {
    ('', ''): [1.0, 2.0, 3.0],           # Constant term
    ('C1', ''): [0.1, 0.2, 0.3],       # Linear term
    ('C1', 'C2'): [0.01, 0.02, 0.03] # Quadratic term (ordered!)
}
poly = POPxfPolynomial(poly_data, degree=2)
print(poly.parameters)  # ('C1', 'C2')

POPxfPolynomialUncertainty

Specialized class for uncertainty polynomials, supporting both parameter-dependent and parameter-independent uncertainties.

Example:

# Parameter-independent uncertainty (constant)
unc_data = [0.1, 0.2, 0.3]
unc = POPxfPolynomialUncertainty(unc_data, degree=2)

# Parameter-dependent uncertainty
unc_data = {
    ('', ''): [0.1, 0.2, 0.3],
    ('cHq3', ''): [0.01, 0.02, 0.03]
}
unc = POPxfPolynomialUncertainty(unc_data, degree=2)

Validation

The validator/parser performs multiple levels of validation:

Schema Validation: Ensures JSON structure conforms to POPxf schema
Mode Detection: Determines SP/FOP mode and checks required fields
Scale Validation: Checks consistency of scale field with mode
Polynomial Format (POPxfParser only): Validates polynomial keys (ordering, length) and values (type, shape)
Array Length (POPxfParser only): Ensures coefficient arrays match metadata specifications
Parameter Consistency (POPxfParser only): Verifies all polynomial parameters are declared

Error Handling

from validator import POPxfValidator, POPxfValidationError
from parser import POPxfParser, POPxfParserError

# Validation only
try:
    validator = POPxfValidator(data)
except POPxfValidationError as e:
    print(f"Validation failed: {e}")

# Full parsing
try:
    parser = POPxfParser(data)
except POPxfValidationError as e:
    # Validation error with detailed path and description
    print(f"Validation failed: {e}")
except POPxfParserError as e:
    # Parsing error (e.g., polynomial initialization)
    print(f"Parsing failed: {e}")

Polynomial Key Format

Polynomial keys must follow specific conventions:

# General format: (param1, param2, ..., paramN, [RI_specifier])
# - N parameters matching polynomial degree
# - Alphabetically ordered
# - Optional real/imaginary specifier as last element

# Valid keys (degree 2):
('', '')              # Constant term (assumed real)
('', '', 'RR')        # Constant term (explicitly real)
('C1', '')          # Linear in cHq1
('C1', 'C2')      # Quadratic (alphabetically ordered)
('C1', 'C2', 'RR') # Quadratic, both real components
('C1', 'C2', 'RI') # Quadratic, cHq1 real, cHq3 imaginary

# Invalid keys:
('C2', 'C1')      # Wrong order! Must be alphabetical
('C1', 'C2', 'C3') # Wrong degree for degree=2

Project Structure

parser/
├── __init__.py          # Package initialization
├── validator.py         # POPxfValidator class (validation only)
├── parser.py            # POPxfParser class (extends POPxfValidator)
├── polynomial.py        # POPxfPolynomial and POPxfPolynomialUncertainty classes
├── schemas.py           # Schema definitions and validators
├── typedmapping.py      # Base class for type-validated dictionaries
├── formatting.py        # JSON formatting utilities
├── bin/                 # Command-line tools
│   └── validate-popxf   # CLI validator and inspector
├── schemas/             # JSON schema files
│   └── popxf-1.0.json
└── examples/            # Example POPxf files
    ├── BR_B0_mumu.json
    ├── BR_Bs_mumu.json
    ├── BR_Bs_mumu_B0_mumu.json
    ├── Gam_Wmunum.json
    ├── R_W_lilj.json
    └── bad/             # Invalid files for testing
        ├── missing_schema.json
        ├── bad_value_schema.json
        └── ... (other test files)

Examples

Command-Line Usage

# Quick validation
python bin/validate-popxf examples/BR_B0_mumu.json

# Detailed inspection
python bin/validate-popxf examples/Gam_Wmunum.json --verbose --show-data

# Batch validation (quiet mode)
for file in examples/*.json; do
    python bin/validate-popxf "$file" --quiet && echo "✓ $file"
done

Python API - Validation Only

Use POPxfValidator when you only need to validate the file structure without parsing polynomials:

import json
from validator import POPxfValidator

# Load and validate
with open('examples/BR_B0_mumu.json') as f:
    validator = POPxfValidator(json.load(f))

# Print summary information
print(validator.info())

# Access metadata
print(f"Mode: {validator.mode}")
print(f"Observable: {validator.metadata['observable_names'][0]}")
print(f"Parameters: {validator.parameters}")

Python API - Full Parsing

Use POPxfParser when you need to work with polynomial data:

import json
from parser import POPxfParser

# Load and parse
with open('examples/BR_B0_mumu.json') as f:
    parser = POPxfParser(json.load(f))

# Print summary information
print(parser.info())

# Access metadata (inherited from POPxfValidator)
print(f"Observable: {parser.metadata['observable_names'][0]}")
print(f"EFT Basis: {parser.metadata['basis']}")
print(f"Scale: {parser.metadata['scale']} GeV")

# Access polynomial objects (POPxfParser-specific)
poly = parser.observable_central
print(f"Polynomial has {len(poly)} terms")
print(f"Parameters used: {poly.parameters}")

for key, coeffs in poly.items():
    print(f"{key}: {coeffs}")

Working with Uncertainties

# Access uncertainties
if hasattr(parser, 'observable_uncertainties'):
    for source, unc_poly in parser.observable_uncertainties.items():
        print(f"\n{source} uncertainty:")
        print(f"  Parameters: {unc_poly.parameters}")
        print(f"  Terms: {len(unc_poly)}")

Serialization

# Convert polynomial to JSON string
json_str = poly.to_jstr()
print(json_str)

# Convert to plain dictionary
poly_dict = poly.to_dict()

# Suppress real/imaginary specifiers in output
json_str = poly.to_jstr(suppress_RI=True)

Requirements

Python 3.7+
numpy
jsonschema

References

POPxf Format Specification: [Link to specification]
JSON Schema: https://json-schema.org/popxf-1.0/json

Contact

For questions or issues, please open an issue on GitHub.

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
bin		bin
examples		examples
schemas		schemas
README.md		README.md
__init__.py		__init__.py
formatting.py		formatting.py
parser.py		parser.py
polynomial.py		polynomial.py
schemas.py		schemas.py
typedmapping.py		typedmapping.py
validator.py		validator.py

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POPxf Parser

Features

Installation

Architecture

POPxfValidator

POPxfParser

Quick Start

Validation Only

Full Parsing

Command-Line Tool

Basic Usage

Command-Line Options

Example Output

Error Handling

Exit Codes

POPxf Modes

Single-Polynomial (SP) Mode

Function-Of-Polynomials (FOP) Mode

Core Classes

POPxfValidator

POPxfParser

POPxfPolynomial

POPxfPolynomialUncertainty

Validation

Error Handling

Polynomial Key Format

Project Structure

Examples

Command-Line Usage

Python API - Validation Only

Python API - Full Parsing

Working with Uncertainties

Serialization

Requirements

References

Contact

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