import matplotlib.pyplot as plt
import numpy as np

from pymoo.model.problem import Problem


class FlowshopScheduling(Problem):

    def __init__(self, processing_times, **kwargs):
        """
        Flowshop scheduling problem.

        Parameters
        ----------
        processing_times : numpy.array
            Matrix where processing_time[i][j] is the processing time for job j on machine i.

        """

        n_machines, n_jobs = processing_times.shape
        self.data = processing_times

        super(FlowshopScheduling, self).__init__(
            n_var=n_jobs,
            n_obj=1,
            xl=0,
            xu=n_machines,
            type_var=np.int,
            elementwise_evaluation=True,
            **kwargs
        )

    def _evaluate(self, x, out, *args, **kwargs):
        out["F"] = self.makespan(x)

    def makespan(self, x):
        machine_times = self.get_machine_times(x)
        # The makespan is the difference between the starting time of the first job and the latest finish time of any
        # job. Minimizing the makespan amounts to minimizing the total time it takes to process all jobs from start
        # to finish.
        makespan = machine_times[-1][-1] + self.data[-1][x[-1]]  # finish time of the last job
        return makespan

    def get_machine_times(self, x):
        n_machines, n_jobs = self.data.shape

        # A 2d array to store the starting time for each job on each machine
        # machine_times[i][j] --> starting time for the j-th job on machine i
        machine_times = [[] for _ in range(n_machines)]

        # Assign the initial job to the machines
        machine_times[0].append(0)
        for i in range(1, n_machines):
            # Start the next job when the previous one is finished
            machine_times[i].append(
                machine_times[i - 1][0] + self.data[i - 1][x[0]]
            )

        # Assign the remaining jobs
        for j in range(1, n_jobs):
            # For the first machine, we can put a job when the previous one is finished
            machine_times[0].append(
                machine_times[0][j - 1] + self.data[0][x[j - 1]]
            )

            # For the remaining machines, the starting time of the current job j is the max of the following two times:
            # 1. The finish time of the previous job on the current machine
            # 2. The finish time of the current job on the previous machine
            for i in range(1, n_machines):
                machine_times[i].append(
                    max(
                        machine_times[i][j - 1] + self.data[i][x[j - 1]],  # 1
                        machine_times[i - 1][j] + self.data[i - 1][x[j]]  # 2
                    )
                )
        return machine_times


def create_random_flowshop_problem(n_machines, n_jobs, seed=None):
    if seed is not None:
        np.random.seed(seed)
    T = np.random.random((n_machines, n_jobs)) * 50 + 50
    return FlowshopScheduling(T)


def visualize(problem, x, path=None, label=True):
    with plt.style.context('ggplot'):
        n_machines, n_jobs = problem.data.shape
        machine_times = problem.get_machine_times(x)

        fig = plt.figure()
        ax = fig.add_subplot(111)

        Y = np.flip(np.arange(n_machines))

        for i in range(n_machines):
            for j in range(n_jobs):
                width = problem.data[i][x[j]]
                left = machine_times[i][j]
                ax.barh(Y[i], width, left=left,
                        align='center', color='gray',
                        edgecolor='black', linewidth=0.8
                        )
                if label:
                    ax.text((left + width / 2), Y[i], str(x[j] + 1), ha='center', va='center', color='white',
                            fontsize=15)
        ax.set_xlabel("Time")
        ax.set_yticks(np.arange(n_machines))
        ax.set_yticklabels(["M%d" % (i + 1) for i in Y])
        ax.set_title("Makespan: %s" % np.round(problem.makespan(x), 3))
        if path is not None:
            plt.savefig(path)
        plt.show()
