"""Define module level fixtures."""

import json
from os.path import join
from typing import List

import pandas as pd
import pytest

from cobra.core import Model, Reaction, Solution
from cobra.util import solver as sutil


# The scipy interface is currently unstable and may yield errors or infeasible
# solutions.
@pytest.fixture(
    scope="session",
    params=[s for s in ["glpk", "cplex", "gurobi", "hybrid"] if s in sutil.solvers],
)
def all_solvers(request) -> List[str]:
    """Return the avaialble solvers."""
    return request.param


@pytest.fixture(
    scope="session",
    params=[s for s in ["cplex", "gurobi", "hybrid"] if s in sutil.solvers],
)
def qp_solvers(request) -> List[str]:
    """Return the available QP solvers."""
    return request.param


@pytest.fixture(scope="module")
def fva_results(data_directory) -> pd.DataFrame:
    """Load and return saved FVA results for textbook model."""
    with open(join(data_directory, "textbook_fva.json"), "r") as infile:
        df = pd.DataFrame(json.load(infile))
    df.sort_index(inplace=True)
    return df[["minimum", "maximum"]]


@pytest.fixture(scope="module")
def pfba_fva_results(data_directory) -> pd.DataFrame:
    """Load and return saved pFBA FVA results for textbook model."""
    with open(join(data_directory, "textbook_pfba_fva.json"), "r") as infile:
        df = pd.DataFrame(json.load(infile))
    df.sort_index(inplace=True)
    return df[["minimum", "maximum"]]


@pytest.fixture(scope="module")
def room_model() -> Model:
    """Generate ROOM model as described in [1]_ .

    References
    ----------
    .. [1] Tomer Shlomi, Omer Berkman and Eytan Ruppin, "Regulatory on/off
     minimization of metabolic flux changes after genetic perturbations",
     PNAS 2005 102 (21) 7695-7700; doi:10.1073/pnas.0406346102

    """
    test_model = Model("papin_2003")

    v_1 = Reaction("v1")
    v_2 = Reaction("v2")
    v_3 = Reaction("v3")
    v_4 = Reaction("v4")
    v_5 = Reaction("v5")
    v_6 = Reaction("v6", upper_bound=0.0)
    b_1 = Reaction("b1", upper_bound=10.0, lower_bound=0.0)
    b_2 = Reaction("b2")
    b_3 = Reaction("b3")

    test_model.add_reactions([v_1, v_2, v_3, v_4, v_5, v_6, b_1, b_2, b_3])

    v_1.reaction = "A -> B"
    v_2.reaction = "2 B -> C + byp"
    v_3.reaction = "2 B + cof -> D"
    v_4.reaction = "D -> E + cof"
    v_5.reaction = "C + cof -> D"
    v_6.reaction = "C -> E"
    b_1.reaction = "-> A"
    b_2.reaction = "E ->"
    b_3.reaction = "byp ->"

    test_model.objective = "b2"

    return test_model


@pytest.fixture(scope="module")
def room_solution() -> Solution:
    """Generate ROOM solution as described in [1]_ .

    References
    ----------
    .. [1] Tomer Shlomi, Omer Berkman and Eytan Ruppin, "Regulatory on/off
     minimization of metabolic flux changes after genetic perturbations",
     PNAS 2005 102 (21) 7695-7700; doi:10.1073/pnas.0406346102

    """
    fluxes = pd.Series(
        {
            "b1": 10.0,
            "b2": 5.0,
            "b3": 5.0,
            "v1": 10.0,
            "v2": 5.0,
            "v3": 0.0,
            "v4": 0.0,
            "v5": 0.0,
            "v6": 5.0,
        }
    )
    reduced_costs = pd.Series(
        {
            "b1": 0.0,
            "b2": 0.0,
            "b3": 0.0,
            "v1": 0.0,
            "v2": 0.0,
            "v3": 0.0,
            "v4": 0.0,
            "v5": 0.0,
            "v6": 0.0,
        }
    )
    shadow_prices = pd.Series(
        {
            "b1": 0.0,
            "b2": 0.0,
            "b3": 0.0,
            "v1": 0.0,
            "v2": 0.0,
            "v3": 0.0,
            "v4": 0.0,
            "v5": 0.0,
            "v6": 0.0,
        }
    )
    sol = Solution(
        objective_value=5.000,
        status="optimal",
        fluxes=fluxes,
        reduced_costs=reduced_costs,
        shadow_prices=shadow_prices,
    )
    return sol