Add support for SymPY for symbolic mathematics that enables algebraic manipulation, equation solving, calculus, and symbolic computation directly in Python.

docs/scientific-packages.md

@@ -67,6 +67,7 @@
 - **Polars** - High-performance DataFrame operations with lazy evaluation
 - **Seaborn** - Statistical data visualization with dataset-oriented interface, automatic confidence intervals, publication-quality themes, colorblind-safe palettes, and comprehensive support for exploratory analysis, distribution comparisons, correlation matrices, regression plots, and multi-panel figures
 - **SimPy** - Process-based discrete-event simulation framework for modeling systems with processes, queues, and resource contention (manufacturing, service operations, network traffic, logistics). Supports generator-based process definition, multiple resource types (Resource, PriorityResource, PreemptiveResource, Container, Store), event-driven scheduling, process interaction mechanisms (signaling, interruption, parallel/sequential execution), real-time simulation synchronized with wall-clock time, and comprehensive monitoring capabilities for utilization, wait times, and queue statistics
+- **SymPy** - Symbolic mathematics in Python for exact computation using mathematical symbols rather than numerical approximations. Provides comprehensive support for symbolic algebra (simplification, expansion, factorization), calculus (derivatives, integrals, limits, series), equation solving (algebraic, differential, systems of equations), matrices and linear algebra (eigenvalues, decompositions, solving linear systems), physics (classical mechanics with Lagrangian/Hamiltonian formulations, quantum mechanics, vector analysis, units), number theory (primes, factorization, modular arithmetic, Diophantine equations), geometry (2D/3D analytic geometry), combinatorics (permutations, combinations, partitions, group theory), logic and sets, statistics (probability distributions, random variables), special functions (gamma, Bessel, orthogonal polynomials), and code generation (lambdify to NumPy/SciPy functions, C/Fortran code generation, LaTeX output for documentation). Emphasizes exact arithmetic using rational numbers and symbolic representations, supports assumptions for improved simplification (positive, real, integer), integrates seamlessly with NumPy/SciPy through lambdify for fast numerical evaluation, and enables symbolic-to-numeric pipelines for scientific computing workflows
 - **ReportLab** - Programmatic PDF generation for reports and documents
 
 ## Phylogenetics & Trees