# Source code for macop.callbacks.MultiCheckpoint

    """Multi Checkpoint class implementation
    """

    # main imports
    import os
    import logging
    import numpy as np

    # module imports
    from .Callback import Callback
    from ..utils.color import macop_text, macop_line

    class MultiCheckpoint(Callback):
        """
        MultiCheckpoint is used for loading previous computations and start again after loading checkpoint

        Attributes:
            algo: {Algorithm} -- main algorithm instance reference
            every: {int} -- checkpoint frequency used (based on number of evaluations)
            filepath: {str} -- file path where checkpoints will be saved
        """
        def run(self):
            """
            Check if necessary to do backup based on `every` variable
            """
            # get current population
            population = self.algo.population

            currentEvaluation = self.algo.getGlobalEvaluation()

            # backup if necessary
            if currentEvaluation % self.every == 0:

                logging.info("Checkpoint is done into " + self.filepath)

                with open(self.filepath, 'w') as f:

                    for solution in population:
                        solutionData = ""
                        solutionSize = len(solution.data)

                        for index, val in enumerate(solution.data):
                            solutionData += str(val)

                            if index < solutionSize - 1:
                                solutionData += ' '

                        line = str(currentEvaluation) + ';'

                        for i in range(len(self.algo.evaluator)):
                            line += str(solution.scores[i]) + ';'

                        line += solutionData + ';\n'

                        f.write(line)

        def load(self):
            """
            Load backup lines as population and set algorithm state (population and pareto front) at this backup
            """
            if os.path.exists(self.filepath):

                logging.info('Load best solution from last checkpoint')
                with open(self.filepath) as f:

                    # read data for each line
                    for i, line in enumerate(f.readlines()):

                        data = line.replace(';\n', '').split(';')

                        # only the first time
                        if i == 0:
                            # get evaluation  information
                            globalEvaluation = int(data[0])

                            if self.algo.parent is not None:
                                self.algo.parent.numberOfEvaluations = globalEvaluation
                            else:
                                self.algo.numberOfEvaluations = globalEvaluation

                        nObjectives = len(self.algo.evaluator)
                        scores = [float(s) for s in data[1:nObjectives + 1]]

                        # get best solution data information
                        solutionData = list(map(int, data[-1].split(' ')))

                        self.algo.population[i].data = np.array(solutionData)
                        self.algo.population[i].scores = scores

                        self.algo.pfPop[i] = self.algo.population[i]

                print(macop_line())
                print(
                    macop_text('Load of available population from `{}`'.format(
                        self.filepath)))
                print(
                    macop_text('Restart algorithm from evaluation {}.'.format(
                        self.algo.numberOfEvaluations)))

            else:
                print(
                    macop_text(
                        'No backup found... Start running algorithm from evaluation 0.'
                    ))
                logging.info(
                    "Can't load backup... Backup filepath not valid in Checkpoint")

            print(macop_line())