finesse.symbols

Symbolic manipulations (expand, collect, etc.) are based on the book:

Cohen JS. Computer algebra and symbolic computation: mathematical methods. First edition, 2003.

Classes

Constant(value[, name])	Defines a constant symbol that can be used in symbolic math.
Function(name, operation, *args)	This is a symbol to represent a mathematical function.
LazySymbol(name, function, *args)	A generic way to make some lazily evaluated symbol.
Matrix(name)	A Matrix symbol.
Resolving()	A special symbol that represents a symbol that is not yet resolved.
Symbol()
Variable(name)	Makes a variable symbol that can be used in symbolic math.

Functions

MAKE_LOP_simplify_truediv()
MAKE_ROP_simplify_truediv()
MAKE_simplify_add(dir)
MAKE_simplify_mul(dir)
MAKE_simplify_neg()
MAKE_simplify_pos()
MAKE_simplify_pow(dir)
MAKE_simplify_sub(dir)
add_sort_key(a)
as_symbol(x)
base_exponent(y)
coefficient_and_term(y)
collect(y)
display(a[, dunder])	For a given Symbol this method will return a human readable string representing the various operations it contains.
eval_symbolic_numpy(a, *keep)
evaluate(x)	Evaluates a symbol or N-dimensional array of symbols.
expand(y)
expand_mul(y)
expand_pow(y)
finesse2sympy(expr[, iter_num])	Notes
is_integer(n)	Checks if n is an integer.
mul_sort_key(a)	Sorting key for multiplication arguments.
np_eval_symbolic_numpy(a, *keep)
operator_add(*args)
operator_mul(*args)
operator_sub(*args)
reduce_mul_args(args)	Sorts and reduces a multiply operation arguments.
simplification([allow_flagged])	When used any symbolic operations will apply various simplification rules rather than recording everything symbolic operation, to preserve intent.
sympy2finesse(expr[, symbol_dict, iter_num])