pydasa 0.4.4

Python package for Dimensional Analysis for Scientific Applications and Software Architecture (PyDASA).

PyDASA

Library to solve software architecture and physical problems with dimensionless analysis and the Pi-Theorem

Epic User Story

As a researcher, engineer, or software architect analyzing complex systems,

I want a comprehensive dimensional analysis library implementing the Buckingham Pi theorem,

So that I can systematically discover dimensionless relationships, validate models, and understand system behavior across physical, computational, and software architecture domains.

Core Capabilities

Dimensional Framework Management

• Manage fundamental dimensions beyond traditional physical units (L, M, T) .to include computational (T, S, N) and software architecture domains (T, D, E, C, A).
• Switch between frameworks for different problem domains.

Parameter and Variable Management

• Define dimensional parameters with complete specifications:
  • Symbolic representation (name, LaTeX symbol).
  • Dimensional formula (e.g., "L*T^-1" for velocity).
  • Numerical ranges (min, max, mean, step)
  • Classification (input, output, control).
  • Statistical distributions and dependencies.

Unit System Integration

• Handle measurements across unit systems (imperial, metric, custom).
• Convert between units while maintaining dimensional consistency.
• Relate measurements to dimensional parameters.

Dimensionless Coefficient Discovery

• Generate dimensionless numbers using the Buckingham Pi theorem:
  1. Build relevance list: Identify mutually independent parameters influencing the phenomenon.
  2. Construct dimensional matrix: Arrange FDUs (rows) and variables (columns) into core and residual matrices.
  3. Transform to identity matrix: Apply linear transformations to the core matrix.
  4. Generate Pi coefficients: Combine residual and unity matrices to produce dimensionless groups.
• Classify coefficients by repeating vs. non-repeating parameters.
• Manage metadata: names, symbols, formulas, and parameter relationships.

Analysis and Simulation

• Verify similitude principles for model scaling and validation.
• Calculate coefficient ranges and parameter influence.
• Run Monte Carlo simulations to quantify uncertainty propagation.
• Perform sensitivity analysis to identify dominant parameters.
• Generate behavioral data for dimensionless relationships.

Data Export and Visualization Integration

• Export data formats compatible with pandas, matplotlib, seaborn.
• Structure results for integration with visualization libraries.
• Provide standardized outputs for dimensionless charts and parameter influence plots.

Emoji

1. TODO 📋
2. WORKING 🔶👨‍💻
3. DONE ✅
4. WARNING ⚠️

Development Status

✅ Completed

• Src: Merged buckingham→dimensional, split config, moved parameter to elements, renamed datastruct→structs and handlers→workflows
• Imports: Fixed 7 files (analysis, workflows, dimensional modules) to use new paths
• Code Quality: Fixed syntax errors, enhanced Enum validation, fixed frozen dataclass issues
• Tests: Updated test_basic.py and test_setup.py, all 16 core tests passing, structure aligned with src
• Decorator Migration: 100% complete - all core classes migrated to decorator-based validation (463+ tests passing)
  • Elements: Variable, Coefficient, Basic, Fundamental, Measurement, Parameter
  • Workflows: SensitivityAnalysis (21/21 tests ✓), MonteCarloSimulation (14/14 tests ✓), AnalysisEngine (27/27 tests ✓)
  • Validation System: Enhanced @validate_emptiness to handle dictionaries, lists, and all collections (not just strings)
  • Package Configuration (v0.3.2):
  • Added package-data to include JSON config files in distribution
  • Exposed __version__ attribute in main module (7/7 package tests passing)
  • Configured semantic versioning for 0.x releases (major_on_zero=false)
  • Updated CHANGELOG.md with recent changes
  • Dimensional Analysis Fix:
  • Fixed empty dimensional columns bug in CUSTOM framework variables
  • Added validation in Matrix.create_matrix() to detect missing dimensions
  • Fixed 5 test files (test_phenomena.py, test_scenario.py, test_simulation.py, test_practical.py, test_influence.py)
  • All variables now properly initialize with schema and parse dimensions (130/130 tests passing)
  • Documentation Structure:
  • Integrated public folder subpages into main index.rst
  • Reorganized navigation: Getting Started, User Guide, Design & Architecture, API Reference, Examples, Development, Project
  • Documentation builds successfully with Sphinx

🔶 In Progress

• Implementing Solver() class in workflows/phenomena.py
• Incremental testing and validation

📋 Pending

• context/: Unit conversion system (conversion.py, system.py, units.py)
• structs/: Complete test coverage for lists, tables, tools modules
• Tests: Coverage for context, structs modules
• Docs: Fill in stub content for documentation pages (basics.rst, architecture.rst, requirements.rst, etc.)

Src Path Structure

1. pydasa

   1. analysis: analysis and simulation modules, pending recheck📋!!!.

      1. scenario.py: contains the DimSensitivity class for understanding variance in the coefficients. ✅
      2. simulation.py: monte carlo simulator for one coefficient (MonteCarloSim class). ✅

      ---

   2. context: unit conversion and measurement context (future scope)

      1. conversion.py: unit conversion handler for the solver. 📋
      2. system.py: system of units management. 📋
      3. units.py: unit definitions and operations. 📋

      ---

   3. core: shared and core capabilities.

      1. basic.py: contains Foundation, SymBasis, IdxBasis classes, shared capabilities for entities with validation. ✅
      2. constants.py: contains predefined dimensional frameworks (PHYSICAL, COMPUTATION, SOFTWARE) and FDU definitions. ✅
      3. io.py: contains all the input/output functions for saving/loading data of the analysis. ✅
      4. setup.py: contains global configuration (Framework, VarCardinality, CoefCardinality, AnaliticMode enums) and PyDASAConfig singleton. ✅
      5. cfg/: configuration files folder
         1. default.json: default framework configurations. ✅

      ---

   4. dimensional: dimensional analysis core, pending recheck📋!!!.

      1. buckingham.py: contains the Coefficient class to represent dimensionless numbers (formerly vashchy.py). ✅
      2. framework.py: contains the DimSchema class to manage and control FDUs in the solving process. UPDATING CODE NOW🔶👨‍💻!!!.
      3. fundamental.py: contains Dimension class for Buckingham Pi-theorem specific operations. ✅
      4. model.py: contains the DimMatrix class to solve the dimensional matrix. ✅

      ---

   5. elements: parameter and variable management with specification classes. ✅

      1. parameter.py: contains Parameter class for dimensional parameter analysis with decorator-based validation. ✅
      2. specs/: specification classes for different aspects of parameters
         1. conceptual.py: contains conceptual specifications (category, schema). ✅
         2. numerical.py: contains numerical specifications (min, max, mean, dev, ranges). ✅
         3. statistical.py: contains statistical specifications (distributions, dependencies). ✅
         4. symbolic.py: contains symbolic specifications (dimensions, units, expressions). ✅

      ---

   6. structs: data structures and utilities, pending extensive tests📋!!!.

      1. lists

         1. arlt.py: arraylist implementation. ✅
         2. dllt.py: double linked list implementation. 📋
         3. ndlt.py: node list for double and single linked lists. ✅
         4. sllt.py: single linked list implementation. ✅

      2. tables

         1. htme.py: entry used in the separate chaining hashtable. ✅
         2. scht.py: separate chaining hashtable implementation. ✅

      3. tools

         1. hashing.py: hashing utilities for data structures. ✅
         2. math.py: mathematical utilities for dimensional analysis. ✅
         3. memory.py: memory management utilities. ✅

      4. types

         1. functions.py: function type definitions and utilities. ✅
         2. generics.py: generic type definitions and type aliases. ✅

      ---

   7. workflows: analysis workflow handlers (formerly tasks). ✅

      1. influence.py: contains the SensitivityAnalysis class (formerly SensitivityHandler) for understanding variance in the coefficients. ✅
      2. phenomena.py: has the main Solver() class of the project. IMPORTANT📋!!!
      3. practical.py: contains the MonteCarloSimulation class (formerly MonteCarloHandler) to control all the Monte Carlo simulations. ✅

      ---

   8. serialization: parsing and serialization utilities. ✅

      1. parser.py: contains LaTeX and formula parsing functions for better representation (formerly utils/latex.py). ✅

      ---

   9. validations: validation decorators, patterns, and error handling. ✅

      1. decorators.py: contains all validation decorators (@validate_type, @validate_emptiness, @validate_choices, @validate_range, @validate_index, @validate_pattern, @validate_custom). Fully implemented with enhanced @validate_emptiness supporting strings, dicts, lists, and all collections. ✅
      2. error.py: contains the generic error_handler() function and inspect_var() for all components. ✅
      3. patterns.py: contains regex patterns for validation and parsing (formerly utils/patterns.py). ✅

      ---

Tests Path Structure

1. pydasa

   1. analysis: shared analytic capabilities, pending updates📋!!!.

      1. test_scenario.py: tests for sensitivity analysis of the Coefficients (DimSensitivity class). ✅
      2. test_simulation.py: tests for the Monte Carlo simulator for one coefficient (MonteCarloSim class). ✅

      ---

   2. context: shared unit of measure and system capabilities.

      1. test_conversion.py: tests for unit conversion handler. 📋
      2. test_system.py: tests for system of units management. 📋
      3. test_units.py: tests for unit definitions and operations. 📋

      ---

   3. core: shared and core capabilities

      1. test_basic.py: tests for the Foundation, SymBasis, IdxBasis classes. ✅
      2. test_constants.py: tests for predefined dimensional frameworks and FDU definitions. ✅
      3. test_io.py: tests for all the input/output functions for saving/loading data. ✅
      4. test_setup.py: tests for global configuration and PyDASAConfig singleton. ✅

      ---

   4. data: test fixtures and sample data

      1. test_data.py: contains test data for all PyDASA tests (fixtures and samples). ✅

      ---

   5. dimensional: main dimensiona analysis capabilities, pending updates📋!!!.

      1. test_buckingham.py: tests for the Coefficient class. ✅
      2. test_framework.py: tests for the DimSchema class to manage and control FDUs. 🔶👨‍💻
      3. test_fundamental.py: tests for the Dimension class. ✅
      4. test_model.py: tests for the DimMatrix class to solve the dimensional matrix. ✅

      ---

   6. elements: parameter and variable tests with specification tests. ✅

      1. test_parameter.py: tests for the Parameter class. ✅
      2. specs/: specification class tests
         1. test_conceptual.py: tests for conceptual specifications. ✅
         2. test_numerical.py: tests for numerical specifications. ✅
         3. test_statistical.py: tests for statistical specifications. ✅
         4. test_symbolic.py: tests for symbolic specifications. ✅

      ---

   7. structs: data structures tests, fundamental for unit of measure and conversions. 📋

      1. lists

         1. test_arlt.py: tests for the arraylist. 📋
         2. test_dllt.py: tests for the double linked list. 📋
         3. test_ndlt.py: tests for the node list for double and single linked. 📋
         4. test_sllt.py: tests for the single linked list. 📋

      2. tables

         1. test_htme.py: tests for the entry useful for the separate chaining hashtable. 📋
         2. test_scht.py: tests for the separate chaining hashtable. 📋

      3. tools

         1. test_hashing.py: tests for hashing utilities. 📋
         2. test_math.py: tests for mathematical utilities. 📋
         3. test_memory.py: tests for memory management utilities. 📋

      4. types

         1. test_functions.py: tests for function type definitions. 📋
         2. test_generics.py: tests for generic type definitions and type aliases. 📋

      ---

   8. workflows (formerly tasks)

      1. test_influence.py: tests for the SensitivityAnalysis class (formerly SensitivityHandler) for understanding variance in the coefficients. All 21 tests passing. ✅
      2. test_phenomena.py: tests for the main Solver() class of the project. 📋
      3. test_practical.py: tests for the MonteCarloSimulation class (formerly MonteCarloHandler) to control all the Monte Carlo simulations. All 14 tests passing. ✅

      ---

   9. serialization: parsing and serialization tests. ✅

      1. test_parser.py: tests for LaTeX and formula parsing functions (formerly test_latex.py). ✅

      ---

   10. validations: validation decorators, patterns, and error handling tests. ✅

       1. test_decorators.py: tests for all validation decorators (@validate_type, @validate_pattern, @validate_choices, etc.). ✅
       2. test_error.py: tests for the generic error_handler() function and inspect_var() for all components. ✅
       3. test_patterns.py: tests for regex patterns for validation and parsing. ✅

       ---