Data_Analysis.py

# -*- coding: utf-8 -*-
"""
Created on Thu Mar 12 16:29:10 2020

@author: SR917
"""

import numpy as np 
import scipy as sp 
import matplotlib.pyplot as plt 
import pandas as pd 
import ast 
import ThreeHiggsModel_Analayse as THMA 
from sympy.abc import symbols 
import time 
 
#import according to how many variables are needed - Ex: for 1D import x, a, b 
t,x,y, z, w, h, a,b,c,d, e, f, g,h, l, m,n = symbols('t,x,y, z, w, h, a,b,c,d, e,f,g,h,l,m,n', real = True) 
 
def tidy_up(string): 
    """ 
    Format the strings of arrays to lists of lists 
    """ 
    temp1 = string.replace("array(","") 
    temp2 = temp1.replace(")", "") 
     
    output = ast.literal_eval(temp2) 
    return output 
 
 
def obtained_roots(filename): 
    """ 
    Obatain roots from our homotopy continuation in format required 
    """ 
    df = pd.read_csv(filename) 
    parameters = [] 
    minima = [] 
    eigenvalues_squared = [] 
    for i in df['Parameters'].values: 
        parameters.append(ast.literal_eval(i)) 
    for i in df["Minima Found"].values: 
        try: 
            minima.append([ast.literal_eval(i)]) 
        except ValueError: 
            minima.append(tidy_up(i)) 
    for i in df["Eigenvalues Squared of Minima"].values: 
        try: 
            eigenvalues_squared.append([ast.literal_eval(i)]) 
        except ValueError: 
            eigenvalues_squared.append(tidy_up(i)) 
     
    return parameters, minima, eigenvalues_squared 
 
def find_statistics_for_min(min1,eig1,param1): 
    ratio_all_1 = [] 
    eigenvals_all_1 = [] 
    sum_square_all_1 = [] 
    parameter_no_result = [] 
    minima_exists = [] 
    parameters_exists = [] 
    cost_function_all = [] 
     
    for i in range(len(min1)): 
        if min1[i] != [0]: 
            exact_min, cost_functioni, ratioi, eigenvali, sum_squarei = THMA.evaluate_statistics(min1[i], eig1[i]) 
            ratio_all_1.append(ratioi) 
            eigenvals_all_1.append(eigenvali) 
            sum_square_all_1.append(sum_squarei) 
            minima_exists.append(exact_min) 
            parameters_exists.append(param1[i]) 
            cost_function_all.append(cost_functioni) 
        else: 
            parameter_no_result.append(param1[i]) 
         
    return cost_function_all, ratio_all_1, eigenvals_all_1, sum_square_all_1, minima_exists, parameters_exists, parameter_no_result 
 
 
def extract_values_Genetic(filename): 
     
    time_start = time.time() 
     
    df = pd.read_csv(filename) 
    parameters = [] 
    minima = [] 
    parameters_no_minima = [] 
    parameters_minima = [] 
    minima_exists = [] 
    minima_exits_exact = [] 
    costs_minima = [] 
    all_costs = [] 
    roots_ratio_val_all = [] 
    closest_eigenvalue_per_min_val_all = [] 
    closest_sum_square_per_min_val_all = [] 
     
         
    for string_i in df['Parameters'].values[1:]: 
        string = string_i[1:-1] 
        string = string.replace('\n','') 
        string = string.split(' ') 
         
        parameters_i = [] 
        for j in string: 
            if len(j) != 0: 
                parameters_i.append(ast.literal_eval(j)) 
         
        parameters.append(parameters_i) 
         
    for i in df["Minima Found"].values[1:]: 
        try: 
            minima.append([ast.literal_eval(i)]) 
        except ValueError: 
            minima.append(tidy_up(i)) 
     
     
    for i, minima_i in enumerate(minima): 
         
        if i%1000 == 0: 
            print(i) 
             
        if minima_i == [0]: 
            parameters_no_minima.append(parameters[i]) 
            all_costs.append(1e5) 
        else: 
            parameters_minima.append(parameters[i]) 
            minima_exists.append(minima_i) 
             
            diff_V = THMA.THDM_diff([x,y,z], *parameters[i]) 
             
            #eigenvalues of each minima found 
            eigenvalues_minima_square = [THMA.potential_eigenvalues([x,y,z], minima_i[j], diff_V) for j in range(len(minima_i))] 
            exact_minimum, cost_function, roots_ratio_val, closest_eigenvalue_per_min_val, closest_sum_square_per_min_val =\ 
            THMA.evaluate_statistics(minima_i, eigenvalues_minima_square) 
             
            all_costs.append(cost_function) 
            costs_minima.append(cost_function) 
            minima_exits_exact.append(exact_minimum) 
            roots_ratio_val_all.append(roots_ratio_val) 
            closest_eigenvalue_per_min_val_all.append(closest_eigenvalue_per_min_val) 
            closest_sum_square_per_min_val_all.append(closest_sum_square_per_min_val) 
          
    time_end = time.time() 
    print('Time taken: {}'.format(time_end - time_start)) 
 
    return parameters, parameters_minima, parameters_no_minima, minima_exists, all_costs, costs_minima, minima_exits_exact, roots_ratio_val_all, closest_eigenvalue_per_min_val_all,closest_sum_square_per_min_val_all