🌳 bertini.function_tree

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class bertini.function_tree.AbstractNode

Raises an exception This class cannot be instantiated from Python

__init__(): Raises an exception This class cannot be instantiated from Python

degree((AbstractNode)self) → int :

compute the algebraic degree of node in a function tree, with respect to all variables. returns one integer. negative is non-algebraic.

degree( (AbstractNode)self, (bertini._pybertini.function_tree.symbol.Variable)var) -> int :: compute the algebraic degree of node in a function tree, with respect to a particular variable. returns one integer. negative is non-algebraic.
degree( (AbstractNode)self, (bertini._pybertini.container.VariableGroup)vars) -> int :: compute the algebraic degree of node in a function tree, with respect to a variable group. returns one integer. negative is non-algebraic.

differentiate((AbstractNode)self) → AbstractNode :

differentiate a node. is with respect to all variables. you get a Jacobian back, which represents derivatives wrt all variables simultaneously.

differentiate( (AbstractNode)arg1, (bertini._pybertini.function_tree.symbol.Variable)self) -> AbstractNode :: differentiate a node with respect to one variable. You get a regular old Node in a Function Tree back.

eval_d((AbstractNode)self) → complex :

evaluate in double precision. uses the values of variables already set in a preceding call to var.set_current_value()

eval_d( (AbstractNode)self, (bertini._pybertini.function_tree.symbol.Variable)var) -> complex :: evaluate the derivative of this node with respect to variable var in double precision. uses the values of variables already set in a preceding call to var.set_current_value()

eval_mp((AbstractNode)self) → bertini._pybertini.multiprec.Complex :

evaluate in multiple precision. uses the values of variables already set in a preceding call to var.set_current_value()

eval_mp( (AbstractNode)self, (bertini._pybertini.function_tree.symbol.Variable)var) -> bertini._pybertini.multiprec.Complex :: evaluate the derivative of this node with respect to variable var in multiple precision. uses the values of variables already set in a preceding call to var.set_current_value()

homogenize((AbstractNode)self, (bertini._pybertini.container.VariableGroup)vars, (bertini._pybertini.function_tree.symbol.Variable)homvar) → None :: Homogenize this function tree with respect to the variables in vars using the homogenizing variables homvar. Essentially, multiply all terms downward so they have the same degree, using homvar to make up the degree defficiency.

is_homogeneous((AbstractNode)self) → bool :

test if this Node is homogeneous with respect to all Variables.

is_homogeneous( (AbstractNode)self, (bertini._pybertini.function_tree.symbol.Variable)var) -> bool :: test if this Node is homogeneous with respect to Variable var.
is_homogeneous( (AbstractNode)self, (bertini._pybertini.container.VariableGroup)vars) -> bool :: test if this Node is homogeneous with respect to the Variables in vars.

is_polynomial((AbstractNode)self) → bool :

test if this Node is polynomial with respect to all Variables.

is_polynomial( (AbstractNode)self, (bertini._pybertini.function_tree.symbol.Variable)var) -> bool :: test if this Node is polynomial with respect to Variable var.
is_polynomial( (AbstractNode)self, (bertini._pybertini.container.VariableGroup)vars) -> bool :: test if this Node is polynomial with respect to Variables vars.

multidegree((AbstractNode)self, (bertini._pybertini.container.VariableGroup)vars) → bertini._pybertini.container.ListOfInt :: Compute an integer vector containing the degrees with respect to the variables in vars. Negative entries indicate non-polynomiality

precision((AbstractNode)self) → int: precision( (AbstractNode)self, (int)precision) -> None

reset((AbstractNode)self) → None :: reset (downward) the values of a function tree so that when the next eval_mp or eval_d is called, the tree re-computes

bertini.function_tree.acos((AbstractNode)arg1) → AbstractNode :: the symbolic arccosine operator

bertini.function_tree.asin((AbstractNode)arg1) → AbstractNode :: the symbolic arcsine operator

bertini.function_tree.atan((AbstractNode)arg1) → AbstractNode :: the symbolic arctangent operator

bertini.function_tree.cos((AbstractNode)arg1) → AbstractNode :: the symbolic cosine operator

bertini.function_tree.exp((AbstractNode)arg1) → AbstractNode :: the symbolic exponential operator

bertini.function_tree.log((AbstractNode)arg1) → AbstractNode :: the symbolic natural log operator

bertini.function_tree.sin((AbstractNode)arg1) → AbstractNode :: the symbolic sine operator

bertini.function_tree.tan((AbstractNode)arg1) → AbstractNode :: the symbolic tangent operator

🌳 bertini.function_tree.symbol

class bertini.function_tree.symbol.AbstractNamedSymbol

Raises an exception This class cannot be instantiated from Python

__init__(): Raises an exception This class cannot be instantiated from Python

class bertini.function_tree.symbol.AbstractNumber

Raises an exception This class cannot be instantiated from Python

__init__(): Raises an exception This class cannot be instantiated from Python

class bertini.function_tree.symbol.AbstractSymbol

Raises an exception This class cannot be instantiated from Python

__init__(): Raises an exception This class cannot be instantiated from Python

class bertini.function_tree.symbol.Differential((object)arg1, (Variable)arg2, (str)arg3) → object

__init__((object)arg1, (Variable)arg2, (str)arg3) → object

get_variable((Differential)arg1) → object

class bertini.function_tree.symbol.E((object)arg1) → object

__init__((object)arg1) → object

class bertini.function_tree.symbol.Float((object)arg1, (bertini._pybertini.multiprec.Float)arg2, (bertini._pybertini.multiprec.Float)arg3) → object