"""
Iterative Proportional Fitting (Fratar)
"""
# -----------------------------------------------------------------------------------------------------------
# Package:    AequilibraE
#
# Original Author:  Pedro Camargo (c@margo.co)
# Contributors:
# Last edited by: Pedro Camargo
#
# Website:    www.AequilibraE.com
# Repository:  https://github.com/AequilibraE/AequilibraE
#
# Created:    29/09/2016
# Updated:    11/08/2017
# Copyright:   (c) AequilibraE authors
# Licence:     See LICENSE.TXT
# -----------------------------------------------------------------------------------------------------------
import os
import sys
from time import perf_counter

import numpy as np
import yaml

from ..matrix import AequilibraeData
from ..matrix import AequilibraeMatrix

sys.dont_write_bytecode = True


class Ipf:
    def __init__(self, **kwargs):

        self.parameters = kwargs.get("parameters", self.get_parameters("ipf"))

        # Seed matrix
        self.matrix = kwargs.get("matrix", None)

        # NaN as zero
        self.nan_as_zero = kwargs.get("nan_as_zero", True)

        # row vector
        self.rows = kwargs.get("rows", None)
        self.row_field = kwargs.get("row_field", None)
        self.output_name = kwargs.get("output", AequilibraeMatrix().random_name())

        # Column vector
        self.columns = kwargs.get("columns", None)
        self.column_field = kwargs.get("column_field", None)

        self.output = None
        self.error = None
        self.__required_parameters = ["convergence level", "max iterations", "balancing tolerance"]
        self.error_free = True
        self.report = ["  #####    IPF computation    #####  ", ""]
        self.gap = None

    def check_data(self):
        self.error = None
        self.check_parameters()

        # check data types
        if not isinstance(self.rows, AequilibraeData):
            raise TypeError("Row vector needs to be an instance of AequilibraeData")

        if not isinstance(self.columns, AequilibraeData):
            raise TypeError("Column vector needs to be an instance of AequilibraeData")

        if not isinstance(self.matrix, AequilibraeMatrix):
            raise TypeError("Seed matrix needs to be an instance of AequilibraeMatrix")

        # Check data type
        if not np.issubdtype(self.matrix.dtype, np.floating):
            raise ValueError("Seed matrix need to be a float type")

        row_data = self.rows.data
        col_data = self.columns.data

        if not np.issubdtype(row_data[self.row_field].dtype, np.floating):
            raise ValueError("production/rows vector must be a float type")

        if not np.issubdtype(col_data[self.column_field].dtype, np.floating):
            raise ValueError("Attraction/columns vector must be a float type")

        # Check data dimensions
        if not np.array_equal(self.rows.index, self.columns.index):
            raise ValueError("Indices from row vector do not match those from column vector")

        if not np.array_equal(self.matrix.index, self.columns.index):
            raise ValueError("Indices from vectors do not match those from seed matrix")

        # Check if matrix was set for computation
        if self.matrix.matrix_view is None:
            raise ValueError("Matrix needs to be set for computation")
        else:
            if len(self.matrix.matrix_view.shape[:]) > 2:
                raise ValueError("Matrix' computational view needs to be set for a single matrix core")

        if self.error is None:
            # check balancing:
            sum_rows = np.nansum(row_data[self.row_field])
            sum_cols = np.nansum(col_data[self.column_field])
            if abs(sum_rows - sum_cols) > self.parameters["balancing tolerance"]:
                self.error = "Vectors are not balanced"
            else:
                # guarantees that they are precisely balanced
                col_data[self.column_field][:] = col_data[self.column_field][:] * (sum_rows / sum_cols)

        if self.error is not None:
            self.error_free = False

    def check_parameters(self):
        for i in self.__required_parameters:
            if i not in self.parameters:
                self.error = "Parameters error. It needs to be a dictionary with the following keys: "
                for t in self.__required_parameters:
                    self.error = self.error + t + ", "
                break

    def fit(self):
        t = perf_counter()
        self.check_data()
        if self.error_free:
            max_iter = self.parameters["max iterations"]
            conv_criteria = self.parameters["convergence level"]

            if self.matrix.omx:
                self.output = AequilibraeMatrix()
                self.output.create_from_omx(self.output.random_name(), self.matrix.file_path,
                                            cores=self.matrix.view_names)
                self.output.computational_view()
            else:
                self.output = self.matrix.copy(self.output_name)
            if self.nan_as_zero:
                self.output.matrix_view[:, :] = np.nan_to_num(self.output.matrix_view)[:, :]

            rows = self.rows.data[self.row_field]
            columns = self.columns.data[self.column_field]
            tot_matrix = np.nansum(self.output.matrix_view[:, :])

            # Reporting
            self.report.append("Target convergence criteria: " + str(conv_criteria))
            self.report.append("Maximum iterations: " + str(max_iter))
            self.report.append("")
            self.report.append("Rows:" + str(self.rows.entries))
            self.report.append("Columns: " + str(self.columns.entries))

            self.report.append("Total of seed matrix: " + "{:28,.4f}".format(float(tot_matrix)))
            self.report.append("Total of target vectors: " + "{:25,.4f}".format(float(np.nansum(rows))))
            self.report.append("")
            self.report.append("Iteration,   Convergence")
            self.gap = conv_criteria + 1

            iter = 0
            while self.gap > conv_criteria and iter < max_iter:
                iter += 1
                # computes factors for zones
                marg_rows = self.tot_rows(self.output.matrix_view[:, :])
                row_factor = self.factor(marg_rows, rows)
                # applies factor
                self.output.matrix_view[:, :] = np.transpose(
                    np.transpose(self.output.matrix_view[:, :]) * np.transpose(row_factor)
                )[:, :]

                # computes factors for columns
                marg_cols = self.tot_columns(self.output.matrix_view[:, :])
                column_factor = self.factor(marg_cols, columns)

                # applies factor
                self.output.matrix_view[:, :] = self.output.matrix_view[:, :] * column_factor

                # increments iterarions and computes errors
                self.gap = max(
                    abs(1 - np.min(row_factor)),
                    abs(np.max(row_factor) - 1),
                    abs(1 - np.min(column_factor)),
                    abs(np.max(column_factor) - 1),
                )

                self.report.append(str(iter) + "   ,   " + str("{:4,.10f}".format(float(np.nansum(self.gap)))))

            self.report.append("")
            self.report.append("Running time: " + str("{:4,.3f}".format(perf_counter() - t)) + "s")

    def tot_rows(self, matrix):
        return np.nansum(matrix, axis=1)

    def tot_columns(self, matrix):
        return np.nansum(matrix, axis=0)

    def factor(self, marginals, targets):
        f = np.divide(targets, marginals)  # We compute the factors
        f[f == np.NINF] = 1  # And treat the errors
        return f

    def get_parameters(self, model):
        path = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
        with open(path + "/parameters.yml", "r") as yml:
            path = yaml.safe_load(yml)
        return path["distribution"][model]
