"""
Test calcul moyennes pour les poursuites d'études
"""
import numpy as np
import pytest

import app.pe.moys.pe_moy
from tests.unit import setup

from app import db
import pandas as pd
import app.pe.moys.pe_moy as pe_moy
import app.pe.moys.pe_rcstag as pe_rcstag
import app.pe.moys.pe_tabletags as pe_tabletags
import app.pe.moys.pe_moytag as pe_moytag


def egalite_df(df1, df2):
    return ((df1 == df2) | (np.isnan(df1) & np.isnan(df2))).all()


# ******************************
# app.pe.moys.pe_moy
# ******************************


class Test_pe_moy:
    infos = {
        "aggregat": "S1",
        "tag": "maths",
        "intitule": "UE1.1",
        "cohorte": "groupe",
    }

    def test_init(self):
        """Test de pe_moy.Moyenne.__init__"""
        notes = pd.Series({1: 10.0, 2: 14.0, 3: np.nan, 4: 0.0})
        moy = pe_moy.Moyenne(notes, Test_pe_moy.infos)
        assert moy.etudids == [1, 2, 3, 4], "Etudids incorrect"
        assert moy.inscrits_ids == [1, 2, 4], "Inscriptions incorrectes"
        # Les notes
        notes_attendues = pd.Series([10.0, 14.0, np.nan, 0.0], index=[1, 2, 3, 4])
        assert "note" in moy.df.columns, "Colonne manquante"
        assert egalite_df(moy.df["note"], notes_attendues), "Notes incorrecte"
        # Les étuds
        nb_etuds = pd.Series([4] * 4, index=[1, 2, 3, 4])
        assert "nb_etuds" in moy.df.columns, "Colonne manquante"
        assert egalite_df(moy.df["nb_etuds"], nb_etuds)
        # Les inscrits
        nb_inscrits = pd.Series([3, 3, np.nan, 3], index=[1, 2, 3, 4])
        assert "nb_inscrits" in moy.df.columns, "Colonne manquante"
        assert egalite_df(moy.df["nb_inscrits"], nb_inscrits)
        # Les classements
        classement = pd.Series([2.0, 1.0, np.nan, 3.0], index=[1, 2, 3, 4])
        assert "classement" in moy.df, "Colonne manquante"
        assert egalite_df(moy.df["classement"], classement), "Classements incorrects"
        # Les rangs
        rang = pd.Series(["2/3", "1/3", "nan", "3/3"], index=[1, 2, 3, 4])
        assert "rang" in moy.df, "Colonne manquante"
        assert moy.df["rang"].isnull().sum() == 0, "Des Nan dans les rangs interdits"
        assert (moy.df["rang"] == rang).any(), "Rangs incorrects"
        # Les mins
        assert "min" in moy.df, "Colonne manquante"
        mina = pd.Series([0.0, 0.0, np.nan, 0.0], index=[1, 2, 3, 4])
        assert egalite_df(moy.df["min"], mina), "Min incorrect"
        # Les max
        assert "max" in moy.df, "Colonne manquante"
        maxa = pd.Series([14.0, 14.0, np.nan, 14.0], index=[1, 2, 3, 4])
        assert egalite_df(moy.df["max"], maxa), "Max incorrect"
        # Les moy
        assert "moy" in moy.df, "Colonne manquante"
        moya = pd.Series([8.0, 8.0, np.nan, 8.0], index=[1, 2, 3, 4])
        assert egalite_df(moy.df["moy"], moya), "Moy incorrect"

    def test_init_ex_aequo(self):
        """Test de pe_moy.Moyenne.__init__ pour des ex-aequo"""
        notes = pd.Series({1: 10.0, 2: 14.0, 3: 10.0, 4: 0.0})
        moy = pe_moy.Moyenne(notes, Test_pe_moy.infos)
        # Les rangs
        rang = pd.Series(["2 ex/4", "1/4", "2 ex/4", "3/4"], index=[1, 2, 3, 4])
        assert moy.df["rang"].isnull().sum() == 0, "Des Nan dans les rangs interdits"
        assert (moy.df["rang"] == rang).any(), "Rangs incorrects"

    @pytest.mark.parametrize(
        "notes, resultat",
        [
            pytest.param(
                pd.Series({1: 10.0, 2: 14.0, 3: np.nan, 4: 0.0}), True, id="avec_notes"
            ),
            pytest.param(pd.Series({1: np.nan, 2: np.nan}), False, id="sans_note"),
            pytest.param(pd.Series({1: 0.0, 2: np.nan}), True, id="avec 0"),
        ],
    )
    def test_has_notes(self, notes, resultat):
        moy = pe_moy.Moyenne(notes, Test_pe_moy.infos)
        assert (
            moy.has_notes() == resultat
        ), "Le test sur la présence de notes est incorrect"

    @pytest.mark.parametrize(
        "aggregat, tag, champ, cohorte, critere, attendu",
        [
            pytest.param(
                "S1", "Math", "UE", "Gr", "note", "S1|Math|UE|Gr|note", id="tous_args"
            ),
            pytest.param(
                None,
                "Math",
                "UE",
                "Gr",
                "note",
                "Math|UE|Gr|note",
                id="aggregat manquant",
            ),
            pytest.param(
                None,
                "Math",
                "UE",
                None,
                "note",
                "Math|UE|note",
                id="aggregat et cohorte manquant",
            ),
        ],
    )
    def test_colonnes_df(self, aggregat, tag, champ, cohorte, critere, attendu):
        descr = app.pe.moys.pe_moy.get_colonne_df(
            aggregat, tag, champ, cohorte, critere
        )
        assert descr == attendu, "Nom de colonne incorrect"

    def to_df(self):
        notes = pd.Series({1: 10.0, 2: 14.0, 3: np.nan, 4: 0.0})
        infos = {
            "aggregat": "S1",
            "tag": "maths",
            "intitule": "Général",
            "cohorte": "promo",
        }
        cols = [
            f"S1|maths|Général|promo|{critere}"
            for critere in ["note", "rang", "min", "max", "moy"]
        ]
        moy = pe_moy.Moyenne(notes, infos)
        df = moy.to_df(with_min_max_moy=True)
        assert (
            list(df.columns) == cols
        ), "Colonnes du df de synthèse pour Excel pas correctes"


# ******************************
# app.pe.moys.pe_moytag
# ******************************
class Test_pe_moytag:
    infos = {"aggregat": "S1", "cohorte": "promo"}

    def test_moyennes_tag__init__(self):
        matrice_notes = pd.DataFrame.from_dict(
            {
                1: [12.0, 14.0, 15.0],
                2: [8.0, np.nan, 12.0],
                3: [0.0, 11.0, 13.0],
                4: [np.nan, np.nan, np.nan],
                5: [np.nan, np.nan, np.nan],
                6: [0.0, 0.0, 0.0],
            },
            orient="index",
            columns=["UE1.1", "UE1.2", "UE1.3"],
        )
        matrice_coeffs = pd.DataFrame.from_dict(
            {
                1: [1, 2, 3],
                2: [2, 10, 6],
                3: [1, 2, np.nan],
                4: [5, 4, 3],
                5: [np.nan, np.nan, np.nan],
                6: [1, 1, 1],
            },
            orient="index",
            columns=["UE1.1", "UE1.2", "UE1.3"],
        )
        moy_tag = pe_moytag.MoyennesTag(
            "maths", None, matrice_notes, matrice_coeffs, Test_pe_moytag.infos
        )
        attendu = pd.Series(
            [
                (12 * 1 + 14 * 2 + 15 * 3) / (1 + 2 + 3),
                (8 * 2 + 12 * 6) / (2 + 6),
                (0 * 1 + 2 * 11) / (1 + 2),
                np.nan,
                np.nan,
                0,
            ],
            index=[1, 2, 3, 4, 5, 6],
        )
        assert egalite_df(moy_tag.notes_gen, attendu), "La moyenne n'est pas correcte"

    def test_to_df_sans_renommage_colonne(self):
        """Test le dataframe de synthèse"""
        infos = {"aggregat": "S1", "cohorte": "promo"}
        matrice_notes = pd.DataFrame.from_dict(
            {
                2: [13.0, 13.0, 13],
                1: [12.0, 14.0, 15.0],
            },
            orient="index",
            columns=["UE1.1", "UE1.2", "UE1.3"],
        )
        matrice_coeffs = pd.DataFrame.from_dict(
            {
                2: [1, 2, 3],
                1: [1, 2, 3],
            },
            orient="index",
            columns=["UE1.1", "UE1.2", "UE1.3"],
        )
        moy_tag = pe_moytag.MoyennesTag(
            "maths", None, matrice_notes, matrice_coeffs, infos
        )
        synthese = moy_tag.to_df(
            aggregat="S1", cohorte="promo", options={"min_max_moy": True}
        )
        colonnes_attendues = []
        for ue in ["UE1.1", "UE1.2", "UE1.3", "Général"]:
            for champ in ["note", "rang", "min", "max", "moy"]:
                colonnes_attendues += [f"S1|maths|{ue}|promo|{champ}"]
        assert (
            list(synthese.columns) == colonnes_attendues
        ), "Les colonnes de synthèse ne sont pas correctes"
        assert list(synthese.index) == [
            1,
            2,
        ], "Les lignes ne sont pas triées par id d'étudiants"

    @pytest.mark.parametrize(
        "colonnes, ajouts, resultat",
        [
            pytest.param(["toto", "titi"], ["tutu"], None, id="pas de modif"),
            pytest.param(["toto", "titi"], ["titi"], ["titi (1)"], id="ajout de (1)"),
            pytest.param(
                ["toto", "toto (1)"], ["toto"], ["toto (2)"], id="ajout de (2)"
            ),
            pytest.param(
                ["toto (1)", "titi (3)"],
                ["toto", "titi"],
                ["toto (4)", "titi (4)"],
                id="ajout multiple",
            ),
        ],
    )
    def test_renomme_colonnes(self, colonnes, ajouts, resultat):
        res = pe_moytag.ajout_numero_a_colonnes(colonnes, ajouts)
        assert res == resultat, "L'ajout d'un numéro au colonne est incorrect"


# ******************************
# app.pe.moys.pe_rcstag
# ******************************


@pytest.mark.parametrize(
    "notes_S1, notes_S2, coeffs_S1, coeffs_S2, "
    "coeffs_rcues_S1, coeffs_rcues_S2, inscr_S1, inscr_S2,"
    "moyenne, coeffs_aggreges",
    [
        pytest.param(
            [17.0, 15.0, 12.0],
            [16.0, 14.0, 13.0],  # notes
            [1.0, 2.0, 3.0],
            [4.0, 5.0, 6.0],  # coeffs moy gen
            [2.0, 4.0, 6.0],
            [8.0, 10.0, 12.0],  # coeffs recus
            [1.0, 1.0, 1.0],
            [1.0, 1.0, 1.0],  # inscr
            [
                (17.0 * 2.0 + 16.0 * 8.0) / (2.0 + 8.0),
                (15.0 * 4.0 + 14.0 * 10.0) / (4.0 + 10.0),
                (12.0 * 6.0 + 13.0 * 12.0) / (6.0 + 12.0),
            ],
            [1.0 + 4.0, 2.0 + 5.0, 3.0 + 6.0],
            id="etudiant_parfait",
        ),
        pytest.param(
            [17.0, 15.0, 12.0],
            [16.0, 14.0, 13.0],  # notes
            [1.0, 2.0, 3.0],
            [4.0, 5.0, 6.0],  # coeffs moy gen
            [2.0, 4.0, 6.0],
            [8.0, 10.0, 12.0],  # coeffs recus
            [np.nan, 1.0, np.nan],
            [1.0, np.nan, np.nan],  # inscr
            [
                (16.0 * 8.0) / (8.0),
                (15.0 * 4.0) / (4.0),
                np.nan,
            ],
            [4.0, 2.0, np.nan],
            id="etudiant_non_inscrit",
        ),
        pytest.param(
            [0.0, 15.0, 0.0],
            [0.0, 0.0, 0.0],  # notes
            [1.0, 2.0, 3.0],
            [4.0, 5.0, 6.0],  # coeffs moy gen
            [2.0, 4.0, 6.0],
            [8.0, 10.0, 12.0],  # coeffs recus
            [np.nan, 1.0, 1.0],
            [1.0, np.nan, 1.0],  # inscr
            [0.0, 15.0, 0.0],
            [4.0, 2.0, 3.0 + 6.0],
            id="etudiant_avec_0",
        ),
    ],
)
def test_compute_moyennes_par_RCS(
    notes_S1,
    notes_S2,
    coeffs_S1,
    coeffs_S2,
    coeffs_rcues_S1,
    coeffs_rcues_S2,
    inscr_S1,
    inscr_S2,
    moyenne,
    coeffs_aggreges,
):
    """Test de pe_rcstag.compute_moyennes_par_RCS"""
    notes_cube = np.stack([np.array([notes_S1]), np.array([notes_S2])], axis=-1)
    # Vérifie les dimensions
    dim1, dim2, dim3 = notes_cube.shape
    assert dim1 == 1, "La dim 0 doit être le nombre d'étudiants"
    assert dim2 == 3, "La dim 1 doit être le nombre d'UEs/Compétences"
    assert dim3 == 2, "La dim 2 doit être le nombre de semestres"

    coeffs_cube = np.stack([np.array([coeffs_S1]), np.array([coeffs_S2])], axis=-1)
    coeffs_rcue_cube = np.stack(
        [np.array([coeffs_rcues_S1]), np.array([coeffs_rcues_S2])], axis=-1
    )
    inscr_cube = np.stack([np.array([inscr_S1]), np.array([inscr_S2])], axis=-1)
    moys, coeffs = pe_rcstag.compute_moyennes_par_RCS(
        notes_cube, coeffs_cube, coeffs_rcue_cube, inscr_cube
    )

    moy_resultat = np.array([moyenne])
    assert (
        (moys == moy_resultat) | (np.isnan(moys) & np.isnan(moy_resultat))
    ).all(), "Moyenne erronée"
    coeffs_resultat = np.array([coeffs_aggreges])
    assert (
        (coeffs == coeffs_resultat) | (np.isnan(coeffs) & np.isnan(coeffs_resultat))
    ).all(), "Coeffs (pour moyenne générale sur toutes les UE) erronés"