# add a function to compte complexity

from get_pareto import Point, ParetoSet
from RPN_to_pytorch import RPN_to_pytorch
from RPN_to_eq import RPN_to_eq
import numpy as np
import matplotlib.pyplot as plt
from S_brute_force import brute_force
from S_combine_pareto import combine_pareto
from S_get_number_DL import get_number_DL
from sympy.parsing.sympy_parser import parse_expr
from sympy import preorder_traversal, count_ops
from S_polyfit import polyfit
from S_get_symbolic_expr_error import get_symbolic_expr_error
from S_add_sym_on_pareto import add_sym_on_pareto
from S_add_snap_expr_on_pareto_polyfit import add_snap_expr_on_pareto_polyfit
import os
from os import path


def run_bf_polyfit(pathdir,pathdir_transformed,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4, output_type=""):
    
#############################################################################################################################
    
    # run BF on the data (+)
    print("Checking for brute force + \n")
    brute_force(pathdir_transformed,filename,BF_try_time,BF_ops_file_type,"+")
    
    try:
        # load the BF output data
        bf_all_output = np.loadtxt("results.dat", dtype="str")
        express = bf_all_output[:,2]
        prefactors = bf_all_output[:,1]
        prefactors = [str(i) for i in prefactors]
        
        # Calculate the complexity of the bf expression the same way as for gradient descent case
        complexity = []
        errors = []
        eqns = []
        for i in range(len(prefactors)):
            try:
                if output_type=="":
                    eqn = prefactors[i] + "+" + RPN_to_eq(express[i])
                elif output_type=="acos":
                    eqn = "cos(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="asin":
                    eqn = "sin(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="atan":
                    eqn = "tan(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="cos":
                    eqn = "acos(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="exp":
                    eqn = "log(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="inverse":
                    eqn = "1/(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="log":
                    eqn = "exp(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="sin":
                    eqn = "acos(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="sqrt":
                    eqn = "(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")**2"
                elif output_type=="squared":
                    eqn = "sqrt(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                elif output_type=="tan":
                    eqn = "atan(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
                
                eqns = eqns + [eqn]
                errors = errors + [get_symbolic_expr_error(pathdir,filename,eqn)]
                expr = parse_expr(eqn)
                is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
                numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
                compl = 0
                for j in numbers_expr:
                    try:
                        compl = compl + get_number_DL(float(j))
                    except:
                        compl = compl + 1000000

                # Add the complexity due to symbols
                n_variables = len(expr.free_symbols)
                n_operations = len(count_ops(expr,visual=True).free_symbols)
                if n_operations!=0 or n_variables!=0:
                    compl = compl + (n_variables+n_operations)*np.log2((n_variables+n_operations))

                complexity = complexity + [compl]
            except:
                continue

        for i in range(len(complexity)):
            PA.add(Point(x=complexity[i], y=errors[i], data=eqns[i]))

        # run gradient descent of BF output parameters and add the results to the Pareto plot
        for i in range(len(express)):
            try:
                bf_gd_update = RPN_to_pytorch(pathdir+filename,eqns[i])
                PA.add(Point(x=bf_gd_update[1],y=bf_gd_update[0],data=bf_gd_update[2]))
            except:
                continue
    except:
        pass

#############################################################################################################################
    # run BF on the data (*)
    print("Checking for brute force * \n")
    brute_force(pathdir_transformed,filename,BF_try_time,BF_ops_file_type,"*")

    try:
        # load the BF output data
        bf_all_output = np.loadtxt("results.dat", dtype="str")
        express = bf_all_output[:,2]
        prefactors = bf_all_output[:,1]
        prefactors = [str(i) for i in prefactors]
        
        # Calculate the complexity of the bf expression the same way as for gradient descent case
        complexity = []
        errors = []
        eqns = []
        for i in range(len(prefactors)):
            try:
                if output_type=="":
                    eqn = prefactors[i] + "*" + RPN_to_eq(express[i])
                elif output_type=="acos":
                    eqn = "cos(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="asin":
                    eqn = "sin(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="atan":
                    eqn = "tan(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="cos":
                    eqn = "acos(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="exp":
                    eqn = "log(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="inverse":
                    eqn = "1/(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="log":
                    eqn = "exp(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="sin":
                    eqn = "acos(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="sqrt":
                    eqn = "(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")**2"
                elif output_type=="squared":
                    eqn = "sqrt(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                elif output_type=="tan":
                    eqn = "atan(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
                
                eqns = eqns + [eqn]
                errors = errors + [get_symbolic_expr_error(pathdir,filename,eqn)]
                expr = parse_expr(eqn)
                is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
                numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
                compl = 0
                for j in numbers_expr:
                    try:
                        compl = compl + get_number_DL(float(j))
                    except:
                        compl = compl + 1000000

                # Add the complexity due to symbols
                n_variables = len(expr.free_symbols)
                n_operations = len(count_ops(expr,visual=True).free_symbols)
                if n_operations!=0 or n_variables!=0:
                    compl = compl + (n_variables+n_operations)*np.log2((n_variables+n_operations))

                complexity = complexity + [compl]
            except:
                continue

        # add the BF output to the Pareto plot
        for i in range(len(complexity)):
            PA.add(Point(x=complexity[i], y=errors[i], data=eqns[i]))

        # run gradient descent of BF output parameters and add the results to the Pareto plot
        for i in range(len(express)):
            try:
                bf_gd_update = RPN_to_pytorch(pathdir+filename,eqns[i])
                PA.add(Point(x=bf_gd_update[1],y=bf_gd_update[0],data=bf_gd_update[2]))
            except:
                continue
    except:
        pass
#############################################################################################################################
    # run polyfit on the data
    print("Checking polyfit \n")
    polyfit_result = polyfit(polyfit_deg, pathdir_transformed+filename)
    eqn = str(polyfit_result[0])
    
    # Calculate the complexity of the polyfit expression the same way as for gradient descent case    
    if output_type=="":
        eqn = eqn
    elif output_type=="acos":
        eqn = "cos(" + eqn + ")"
    elif output_type=="asin":
        eqn = "sin(" + eqn + ")" 
    elif output_type=="atan":
        eqn = "tan(" + eqn + ")"
    elif output_type=="cos":
        eqn = "acos(" + eqn + ")"
    elif output_type=="exp":
        eqn = "log(" + eqn + ")"
    elif output_type=="inverse":
        eqn = "1/(" + eqn + ")"
    elif output_type=="log":
        eqn = "exp(" + eqn + ")"
    elif output_type=="sin":
        eqn = "acos(" + eqn + ")"
    elif output_type=="sqrt":
        eqn = "(" + eqn + ")**2"
    elif output_type=="squared":
        eqn = "sqrt(" + eqn + ")"
    elif output_type=="tan":
        eqn = "atan(" + eqn + ")"
    
    polyfit_err = get_symbolic_expr_error(pathdir,filename,eqn)
    expr = parse_expr(eqn)
    is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
    numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
    complexity = 0
    for j in numbers_expr:
        complexity = complexity + get_number_DL(float(j))
    try:
        # Add the complexity due to symbols
        n_variables = len(polyfit_result[0].free_symbols)
        n_operations = len(count_ops(polyfit_result[0],visual=True).free_symbols)
        if n_operations!=0 or n_variables!=0:
            complexity = complexity + (n_variables+n_operations)*np.log2((n_variables+n_operations))
    except:
        pass

    
    #run zero snap on polyfit output
    PA_poly = ParetoSet()
    PA_poly.add(Point(x=complexity, y=polyfit_err, data=str(eqn)))
    PA_poly = add_snap_expr_on_pareto_polyfit(pathdir, filename, str(eqn), PA_poly)
    
    
    for l in range(len(PA_poly.get_pareto_points())):
        PA.add(Point(PA_poly.get_pareto_points()[l][0],PA_poly.get_pareto_points()[l][1],PA_poly.get_pareto_points()[l][2]))

    print("Complexity  RMSE  Expression")
    for pareto_i in range(len(PA.get_pareto_points())):
        print(PA.get_pareto_points()[pareto_i])
    
    return PA