mathjson-solver 1.19.0

Utilities for MathJSON evaluation

MathJSON Solver

A reliable Python library for numerically evaluating mathematical expressions in MathJSON format. Perfect for applications that need to safely execute user-provided formulas, calculate dynamic equations, or process mathematical data.

What is MathJSON? MathJSON represents mathematical expressions as JSON arrays, like ["Add", 1, 2, 3] for 1+2+3. This format is safe, structured, and easy to generate programmatically.

Table of Contents

• Installation
• Quick Start
• Supported Operations
• Error Handling
• Use Cases
• Testing
• Community
• Contributing

Installation

pip install mathjson-solver

Requirements: Python 3.7+

Optional: numpy (only required for TrapezoidalIntegrate function)

Quick Start

from mathjson_solver import create_solver, MathJSONException

# Define variables and create solver
parameters = {"x": 2, "y": 3}
solver = create_solver(parameters)

# Evaluate expressions
basic_math = solver(["Add", "x", "y", 4])
print(basic_math)  # 9 (because 2+3+4=9)

# More complex expressions
result = solver(["Multiply", ["Add", "x", 1], ["Subtract", "y", 1]])
print(result)  # 6 (because (2+1) * (3-1) = 6)

# Handle errors gracefully
try:
    solver(["Divide", 1, 0])
except MathJSONException as e:
    print(f"Math error: {e}")
    # Math error: Problem in Divide. ['Divide', 1, 0]. division by zero

Think Functional: MathJSON Solver embraces functional programming principles. Instead of writing loops and modifying variables, you compose expressions that transform data. Functions like Map, Reduce, and Filter let you process arrays elegantly, while immutable operations ensure predictable, side-effect-free calculations. Don't worry if you're new to functional programming — the examples will guide you naturally into this powerful paradigm.

# Functional approach: transform data with expressions
solver(["Map", ["Array", 1, 2, 3, 4], ["Multiply"], 2])  # [2, 4, 6, 8]
solver(["Reduce", ["Array", 1, 2, 3, 4], 0, ["Add", "acc", "item"],
        ["Variable", "acc"], ["Variable", "item"], ["Variable", "i"]])  # 10

Supported Operations

The library supports a comprehensive set of mathematical operations:

• Arithmetic: Add, Sum, Subtract, Multiply, Divide, Negate, Power, Square, Root, Sqrt, Abs, Round, Floor, Ceil
• Trigonometry: Sin, Cos, Tan, Arcsin, Arccos, Arctan
• Logarithms: Log, Log2, Log10, Exp
• Comparison: Equal, StrictEqual, NotEqual, Greater, GreaterEqual, Less, LessEqual
• Logic & Sets: Any, All, Not, And, Or, In, NotIn, ContainsAnyOf, ContainsAllOf, ContainsNoneOf
• Statistics: Average, Max, Min, Median, Length
• Functional Programming: Map, Reduce, Filter
• Arrays: Array creation, GenerateRange, AtIndex, Slice, Appended, CumulativeSum, CumulativeProduct
• Control Flow: If statements, Switch-Case, Constants definition
• Type Conversion: Int, Float, Str, IsDefined
• Date/Time: Strptime, Strftime, Today, Now, TimeDelta functions (Weeks, Days, Hours, Minutes)
• Integration: TrapezoidalIntegrate (requires numpy), Interp, FindIntervalIndex, Variable references
• Advanced: HasMatchingSublist for pattern matching
• Constants: Pi

View complete documentation with examples →

Error Handling

MathJSON Solver raises MathJSONException for invalid expressions or mathematical errors:

from mathjson_solver import create_solver, MathJSONException

solver = create_solver({})

# Handle specific math errors
try:
    result = solver(["Sqrt", -1])  # Invalid: square root of negative
except MathJSONException as e:
    print(f"Cannot evaluate: {e}")

# Handle malformed expressions
try:
    result = solver(["UnknownFunction", 1, 2])
except MathJSONException as e:
    print(f"Unsupported operation: {e}")

Use Cases

• Dynamic Formulas: Let users create custom calculations in web applications
• Scientific Computing: Evaluate mathematical models with variable parameters
• Business Logic: Process complex pricing rules or scoring algorithms
• Data Processing: Apply mathematical transformations to datasets
• Health Applications: Calculate medical scores, dosages, or risk assessments

Testing

Install development dependencies and run tests:

# Install pytest if not already installed
pip install pytest

# Run tests from project directory
pytest

# Run with coverage
pytest --cov=mathjson_solver

Community

• Questions & Discussion: GitHub Issues
• Bug Reports & Feature Requests: GitHub Issues
• Documentation: Complete Function Reference

Contributing

We welcome contributions! Please feel free to:

• Report bugs or request features via GitHub Issues
• Submit pull requests with improvements

License

View license information

References

This implementation was inspired by the CortexJS Compute Engine, though designed as an independent implementation focused on our specific use cases.

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