import os
from models.LIFACnoise import LifacNoiseModel
import numpy as np


def get_best_fit(folder_path):
    min_err = np.inf
    min_item = ""
    for item in os.listdir(folder_path):
        err = float(item.split("_")[-1])
        if err < min_err:
            min_err = err
            min_item = item

    return ModelFit(os.path.join(folder_path, min_item))


class ModelFit:

    def __init__(self, folder_path):
        self.path = folder_path
        self.parameter_file_name = "parameters_info.txt"
        self.value_file = "value_comparision.tsv"
        self.fi_comp_img = "fi_curve_comparision.png"
        self.isi_hist_img = "isi-histogram.png"
        self.isi_hist_comp_img = "isi-histogram_comparision.png"

    def get_final_parameters(self):
        par_file_path = os.path.join(self.path, self.parameter_file_name)
        with open(par_file_path, 'r') as par_file:
            for line in par_file:
                line = line.strip().split('\t')

                if line[0] == "final_parameters:":
                    return dict(line[1])

        print("Final parameters not found! - ", self.path)
        return {}

    def get_start_parameters(self):
        par_file_path = os.path.join(self.path, self.parameter_file_name)
        with open(par_file_path, 'r') as par_file:
            for line in par_file:
                line = line.strip().split('\t')

                if line[0] == "start_parameters:":
                    return dict(line[1])
        print("Start parameters not found! - ", self.path)
        return {}

    def get_behaviour_values(self):
        values_file_path = os.path.join(self.path, self.value_file)
        cell_values = {}
        model_values = {}
        with open(values_file_path, 'r') as val_file:
            line = val_file.readline()  # ignore headers
            for line in val_file:
                line = line.strip().split('\t')
                cell_values[line[0]] = float(line[1])
                model_values[line[0]] = float(line[2])

        return cell_values, model_values

    def get_fi_curve_comparision_image(self):
        path = os.path.join(self.path, self.fi_comp_img)
        if os.path.exists(path):
            return path
        else:
            raise FileNotFoundError("Fi-curve comparision image is missing. - " + self.path)

    def get_isi_histogram_image(self):
        path = os.path.join(self.path, self.isi_hist_img)
        if os.path.exists(path):
            return path
        else:
            raise FileNotFoundError("Isi-histogram image is missing. - " + self.path)

    def get_error_value(self):
        return self.path.split("_")[-1]

    def get_model(self):
        return LifacNoiseModel(self.get_final_parameters())