Source code for k1lib.graphEqn

# AUTOGENERATED FILE! PLEASE DON'T EDIT
"""This module is for creating dynamic graphs using plain old
equations. Don't use this, still experimental"""
from typing import Callable as _Callable
import k1lib as _k1lib
[docs]class Expression: def __init__(self, a:"Variable", b:"Variable", operation:_Callable[[float, float], float]): self.a = a; self.b = b; self.operation = operation @property def resolved(self): return self.a.resolved and self.b.resolved @property def value(self): return self.operation(self.a.value, self.b.value)
[docs] def applyF(self, f:_Callable[["Variable"], None]): self.a.applyF(f); self.b.applyF(f)
def _op2(a, b, operation): a = a if isinstance(a, Variable) else Constant(a) b = b if isinstance(b, Variable) else Constant(b) answer = Variable(); answer.expr = Expression(a, b, operation) if answer.expr.resolved: answer.value = answer.expr.value return answer
[docs]class Variable: idx = 0 def __init__(self): self.__class__.idx += 1; self.variableName = f"V{self.__class__.idx}" self.expr:Expression = None self.value:float = None # not None, then already resolved self.isConstant = False # to know if the value above is resolved, or is truely a literal number self.trial:int = 0 # current resolve trial number
[docs] def unresolve(self): # restores variable if it's not a constant self.value = self.value if self.isConstant else None
@property def resolved(self): return self.value != None
[docs] def applyF(self, f:_Callable[["Variable"], None]): # apply an operation to variable and its dependencies f(self); self.expr.applyF(f) if self.expr != None else 0
[docs] def resolve(self, trial:int) -> bool: # attempt to resolve variable. Return true if tree changes if self.trial >= trial or self.resolved or self.expr == None: return False changed = self.expr.a.resolve(trial) or self.expr.b.resolve(trial) # try to resolve dependencies first self.trial = trial if self.expr.resolved: self.value = self.expr.value; changed = True return changed
[docs] def simplify(self, printStuff:bool=False): # simplify system before solving self.applyF(lambda v: setattr(v, "trial", 0)); trial = 2 while self.resolve(trial): trial += 1 if printStuff and not self.resolved: print("Can't find a solution")
[docs] def solve(self, x): # try to solve this tree, given independent variable with specific value self.applyF(lambda v: v.unresolve()); self.simplify(); leaves = self.leaves if len(leaves) > 1: raise Exception(f"System of equation has {len(leaves)} indenpendent variables. Please constrain system more!") elif len(leaves) == 1: next(iter(leaves)).value = x self.simplify(True)
@property def _leaves(self): # get dependent variables's that does not have an expression linked to it if self.resolved: return [] if self.expr == None: return [self] else: return self.expr.a._leaves + self.expr.b._leaves @property def leaves(self): return list(set(self._leaves)) def __call__(self, x): self.solve(x); return self.value def __add__(self, variable): return _op2(self, variable, lambda a, b: a + b) def __sub__(self, variable): return _op2(self, variable, lambda a, b: a - b) def __mul__(self, variable): return _op2(self, variable, lambda a, b: a * b) def __truediv__(self, variable): return _op2(self, variable, lambda a, b: a / b) def __radd__(self, variable): return _op2(variable, self, lambda a, b: a + b) def __rsub__(self, variable): return _op2(variable, self, lambda a, b: a - b) def __rmul__(self, variable): return _op2(variable, self, lambda a, b: a * b) def __rtruediv__(self, variable): return _op2(variable, self, lambda a, b: a / b) def __repr__(self): return f"{self.value}" if self.resolved else f"Not resolved: {self.variableName}" def __int__(self): return self.value def __float__(self): return self.value
[docs]class Constant(Variable): def __init__(self, value:float): super().__init__() self.value = value self.isConstant = True