# Distribution.py -- Pamhyr
# Copyright (C) 2026  INRAE
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

# -*- coding: utf-8 -*-

import struct
import logging
import itertools
import numpy as np

from tools import logger_exception

from Model.Except import NotImplementedMethodeError
from Model.Tools.PamhyrDB import SQLSubModel
from Model.Scenario import Scenario

logger = logging.getLogger()


class Distribution(SQLSubModel):
    _sub_classes = []

    def __init__(self, id: int = -1,
                 name: str = "", script: str = "",
                 status=None):
        super(Distribution, self).__init__(
            id=id, status=status,
            owner_scenario=0
        )

        self._name = name
        self._type = "generic"
        self._script = script
        self._labels = []
        self._default_values = []
        self._params_checkers = []
        self._params_tips = []

    @property
    def name(self):
        return self._name

    @property
    def labels(self):
        return self._labels

    @property
    def default_values(self):
        return self._default_values

    @classmethod
    def _db_create(cls, execute, ext=""):
        execute(f"""
          CREATE TABLE ensemble_distribution{ext} (
            {cls.create_db_add_pamhyr_id()},
            deleted BOOLEAN NOT NULL DEFAULT FALSE,
            name TEXT NOT NULL,
            script TEXT NOT NULL,
            PRIMARY KEY(pamhyr_id)
          )
        """)

        if ext == "_tmp":
            return True

        return cls._create_submodel(execute)

    @classmethod
    def _db_update(cls, execute, version, data=None):
        major, minor, release = version.strip().split(".")
        if major == "0" and int(minor) < 2:
            cls._db_create(execute)
        elif major == "0" and int(minor) == 2:
            if int(release) < 8:
                cls._db_create(execute)

        return cls._update_submodel(execute, version, data)

    @classmethod
    def _db_load(cls, execute, data=None):
        new = []
        scenario = data["scenario"]
        loaded = data['loaded_pid']

        table = execute(
            "SELECT pamhyr_id, deleted, name, script " +
            "FROM ensemble_distribution " +
            f"WHERE pamhyr_id NOT IN ({', '.join(map(str, loaded))})"
        )

        for row in table:
            it = iter(row)

            id = next(it)
            deleted = (next(it) == 1)
            name = next(it)
            script = next(it)

            new_distribution = cls(
                id, name=name, script=script,
                status=data["status"]
            )
            if deleted:
                new_distribution.set_as_deleted()

            loaded.add(id)
            new.append(new_distribution)

        return new

    def _db_save(self, execute, data=None):
        # Only save custom distribution
        if self._type != "custom":
            return True

        if not self.must_be_saved():
            return True

        execute(
            "INSERT INTO " +
            "ensemble_distribution(pamhyr_id, deleted, " +
            "  name, script) " +
            "VALUES (?, ?, ?, ?)",
            self.pamhyr_id, self.is_deleted(),
            self._name, self._script
        )

        return True

    def _data_traversal(self,
                        predicate=lambda obj, data: True,
                        modifier=lambda obj, data: None,
                        data={}):
        if predicate(self, data):
            modifier(self, data)

    def is_valid_parameters(self, parameters):
        valid = True
        tips = []

        for i, checker in enumerate(self._params_checkers):
            ok = checker(parameters)
            if not ok:
                tips.append(self._params_tips[i])
                valid = False

        return (valid, tips)

    def check_params(self, params):
        return True

    def random(self, r):
        raise NotImplementedMethodeError(cls, cls._load)


###############################
# DISTRIBUTION IMPLEMENTATION #
###############################

class Uniform(Distribution):
    _sub_classes = []

    def __init__(self, id: int = -1,
                 name: str = "uniform", script: str = "",
                 status=None):
        super(Uniform, self).__init__(
            id=id, status=status,
            name=name, script=script
        )

        self._labels = ["low", "high"]
        self._default_values = [20.0, 40.0]
        self._params_checkers = [self.check_params, self.check_params]
        self._params_tips = ["uniform_low_tips", "uniform_high_tips"]

    def check_params(self, params):
        return (params[0] < params[1])

    def random(self, params, nb):
        lst = params.copy()
        lst.append(nb)

        return np.random.uniform(*lst)


class Normal(Distribution):
    _sub_classes = []

    def __init__(self, id: int = -1,
                 name: str = "normal", script: str = "",
                 status=None):
        super(Normal, self).__init__(
            id=id, status=status,
            name=name, script=script
        )

        self._labels = ["loc", "scale"]
        self._default_values = [30.0, 5.0]
        self._params_checkers = [self.check_params, self.check_scale]
        self._params_tips = ["normal_loc_tips", "normal_scale_tips"]

    def check_scale(self, params):
        return 0 <= params[1]

    def random(self, params, nb):
        lst = params.copy()
        lst.append(nb)

        return np.random.normal(*lst)


class Binomial(Distribution):
    _sub_classes = []

    def __init__(self, id: int = -1,
                 name: str = "binomial", script: str = "",
                 status=None):
        super(Binomial, self).__init__(
            id=id, status=status,
            name=name, script=script
        )

        self._labels = ["binomial_n", "binomial_p"]
        self._default_values = [100.0, 0.3]
        self._params_checkers = [self.check_n, self.check_p]
        self._params_tips = ["binomial_n_tips", "binomial_p_tips"]

    def check_n(self, params):
        n = params[0]
        return (0 <= n) and (n == round(n))

    def check_p(self, params):
        p = params[1]
        return 0 <= p <= 1

    def random(self, params, nb):
        lst = params.copy()
        lst.append(nb)

        return np.random.binomial(*lst)


class Poisson(Distribution):
    _sub_classes = []

    def __init__(self, id: int = -1,
                 name: str = "poisson", script: str = "",
                 status=None):
        super(Poisson, self).__init__(
            id=id, status=status,
            name=name, script=script
        )

        self._labels = ["poisson_lam"]
        self._default_values = [5.0]
        self._params_checkers = [self.check_lam]
        self._params_tips = ["poisson_lam_tips"]

    def check_lam(self, params):
        lam = params[0]
        return 0 <= lam

    def random(self, params, nb):
        lst = params.copy()
        lst.append(nb)

        return np.random.poisson(*lst)


class Custom(Distribution):
    _sub_classes = []

    def __init__(self, id: int = -1,
                 name: str = "", script: str = "",
                 status=None):
        super(Custom, self).__init__(
            id=id, status=status,
            name=name, script=script
        )

        self._labels = ["custom"]
        self._type = "custom"
        self._default_values = [""]
        self._params_checkers = [self.check_params]
        self._params_tips = ["custom_params_tips"]

    def random(self, params, nb):
        try:
            # Run script to (re)define the sample distribution
            f = eval(self._script)
            return f(params, nb)
        except Exception as e:
            logger_exception(e)
            return None         # TODO: Raise helpful exception