import shutil

import pytest
from pytest import approx
import pandas as pd

import calliope
from calliope.test.common.util import check_error_or_warning


class TestModelPreproccesing:
    def test_preprocess_national_scale(self):
        calliope.examples.national_scale()

    def test_preprocess_time_clustering(self):
        calliope.examples.time_clustering()

    def test_preprocess_time_resampling(self):
        calliope.examples.time_resampling()

    def test_preprocess_urban_scale(self):
        calliope.examples.urban_scale()

    @pytest.mark.filterwarnings("ignore:(?s).*Integer:calliope.exceptions.ModelWarning")
    def test_preprocess_milp(self):
        calliope.examples.milp()

    def test_preprocess_operate(self):
        calliope.examples.operate()

    def test_preprocess_time_masking(self):
        calliope.examples.time_masking()


class TestNationalScaleExampleModelSenseChecks:
    def example_tester(self, solver='cbc', solver_io=None):
        override = {
            'model.subset_time': '2005-01-01',
            'run.solver': solver,
        }

        if solver_io:
            override['run.solver_io'] = solver_io

        model = calliope.examples.national_scale(override_dict=override)
        model.run()

        assert model.results.storage_cap.to_pandas()['region1-1::csp'] == approx(45129.950)
        assert model.results.storage_cap.to_pandas()['region2::battery'] == approx(6675.173)

        assert model.results.energy_cap.to_pandas()['region1-1::csp'] == approx(4626.588)
        assert model.results.energy_cap.to_pandas()['region2::battery'] == approx(1000)
        assert model.results.energy_cap.to_pandas()['region1::ccgt'] == approx(30000)

        assert float(model.results.cost.sum()) == approx(38988.7442)

        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.063543, abs=0.000001)
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.2642256, abs=0.000001)

    def test_nationalscale_example_results_cbc(self):
        self.example_tester()

    def test_nationalscale_example_results_gurobi(self):
        try:
            import gurobipy  # pylint: disable=unused-import
            self.example_tester(solver='gurobi', solver_io='python')
        except ImportError:
            pytest.skip('Gurobi not installed')

    def test_nationalscale_example_results_cplex(self):
        if shutil.which('cplex'):
            self.example_tester(solver='cplex')
        else:
            pytest.skip('CPLEX not installed')

    def test_nationalscale_example_results_glpk(self):
        if shutil.which('glpsol'):
            self.example_tester(solver='glpk')
        else:
            pytest.skip('GLPK not installed')

    def test_considers_supply_generation_only_in_total_levelised_cost(self):
        # calculation of expected value:
        # costs = model.get_formatted_array("cost").sum(dim="locs")
        # gen = model.get_formatted_array("carrier_prod").sum(dim=["timesteps", "locs"])
        # lcoe = costs.sum(dim="techs") / gen.sel(techs=["ccgt", "csp"]).sum(dim="techs")
        model = calliope.examples.national_scale()
        model.run()

        assert model.results.total_levelised_cost.item() == approx(0.067005, abs=1e-5)

    def test_fails_gracefully_without_timeseries(self):
        override = {
            "locations.region1.techs.demand_power.constraints.resource": -200,
            "locations.region2.techs.demand_power.constraints.resource": -400,
            "techs.csp.constraints.resource": 100
        }
        with pytest.raises(calliope.exceptions.ModelError):
            calliope.examples.national_scale(override_dict=override)


class TestNationalScaleExampleModelInfeasibility:
    def example_tester(self):

        model = calliope.examples.national_scale(
            scenario='check_feasibility',
            override_dict={'run.cyclic_storage': False}
        )

        model.run()

        assert model.results.attrs['termination_condition'] in ['infeasible', 'other']  # glpk gives 'other' as result

        assert 'systemwide_levelised_cost' not in model.results.data_vars
        assert 'systemwide_capacity_factor' not in model.results.data_vars

    def test_nationalscale_example_results_cbc(self):
        self.example_tester()


class TestNationalScaleExampleModelOperate:
    def example_tester(self):
        with pytest.warns(calliope.exceptions.ModelWarning) as excinfo:
            model = calliope.examples.national_scale(
                override_dict={'model.subset_time': ['2005-01-01', '2005-01-03']},
                scenario='operate')
            model.run()

        expected_warnings = [
            'Energy capacity constraint removed from region1::demand_power as force_resource is applied',
            'Energy capacity constraint removed from region2::demand_power as force_resource is applied',
            'Resource capacity constraint defined and set to infinity for all supply_plus techs'
        ]

        assert check_error_or_warning(excinfo, expected_warnings)
        assert all(model.results.timesteps == pd.date_range('2005-01', '2005-01-03 23:00:00', freq='H'))

    def test_nationalscale_example_results_cbc(self):
        self.example_tester()


class TestNationalScaleResampledExampleModelSenseChecks:
    def example_tester(self, solver='cbc', solver_io=None):
        override = {
            'model.subset_time': '2005-01-01',
            'run.solver': solver,
        }

        if solver_io:
            override['run.solver_io'] = solver_io

        model = calliope.examples.time_resampling(override_dict=override)
        model.run()

        assert model.results.storage_cap.to_pandas()['region1-1::csp'] == approx(23563.444)
        assert model.results.storage_cap.to_pandas()['region2::battery'] == approx(6315.78947)

        assert model.results.energy_cap.to_pandas()['region1-1::csp'] == approx(1440.8377)
        assert model.results.energy_cap.to_pandas()['region2::battery'] == approx(1000)
        assert model.results.energy_cap.to_pandas()['region1::ccgt'] == approx(30000)

        assert float(model.results.cost.sum()) == approx(37344.221869)

        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.063543, abs=0.000001)
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.25, abs=0.000001)

    def test_nationalscale_resampled_example_results_cbc(self):
        self.example_tester()

    def test_nationalscale_resampled_example_results_glpk(self):
        if shutil.which('glpsol'):
            self.example_tester(solver='glpk')
        else:
            pytest.skip('GLPK not installed')


class TestNationalScaleClusteredExampleModelSenseChecks:
    def model_runner(self, solver='cbc', solver_io=None,
                     how='closest', storage_inter_cluster=False,
                     cyclic=False, storage=True):
        override = {
            'model.time.function_options': {
                'how': how, 'storage_inter_cluster': storage_inter_cluster
            },
            'run.solver': solver,
            'run.cyclic_storage': cyclic
        }
        if storage is False:
            override.update({
                'techs.battery.exists': False,
                'techs.csp.exists': False
            })

        if solver_io:
            override['run.solver_io'] = solver_io

        model = calliope.examples.time_clustering(override_dict=override)
        model.run()

        return model

    def example_tester_closest(self, solver='cbc', solver_io=None):
        model = self.model_runner(solver=solver, solver_io=solver_io, how='closest')
        # Full 1-hourly model run: 22312488.670967
        assert float(model.results.cost.sum()) == approx(49670627.15297682)

        # Full 1-hourly model run: 0.296973
        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.137105, abs=0.000001)

        # Full 1-hourly model run: 0.064362
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.064501, abs=0.000001)

    def example_tester_mean(self, solver='cbc', solver_io=None):
        model = self.model_runner(solver=solver, solver_io=solver_io, how='mean')
        # Full 1-hourly model run: 22312488.670967
        assert float(model.results.cost.sum()) == approx(22172253.328)

        # Full 1-hourly model run: 0.296973
        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.127783, abs=0.000001)

        # Full 1-hourly model run: 0.064362
        assert float(
            model.results.systemwide_capacity_factor.loc[dict(carriers='power')].to_pandas().T['battery']
        ) == approx(0.044458, abs=0.000001)

    def example_tester_storage_inter_cluster(self):
        model = self.model_runner(storage_inter_cluster=True)

        # Full 1-hourly model run: 22312488.670967
        assert float(model.results.cost.sum()) == approx(21825515.304)

        # Full 1-hourly model run: 0.296973
        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.100760, abs=0.000001)

        # Full 1-hourly model run: 0.064362
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.091036, abs=0.000001)

    def test_nationalscale_clustered_example_closest_results_cbc(self):
        self.example_tester_closest()

    def test_nationalscale_clustered_example_closest_results_glpk(self):
        if shutil.which('glpsol'):
            self.example_tester_closest(solver='glpk')
        else:
            pytest.skip('GLPK not installed')

    def test_nationalscale_clustered_example_mean_results_cbc(self):
        self.example_tester_mean()

    @pytest.mark.skip(reason='GLPK is useless and delivering different results on different operating systems')
    def test_nationalscale_clustered_example_mean_results_glpk(self):
        if shutil.which('glpsol'):
            self.example_tester_mean(solver='glpk')
        else:
            pytest.skip('GLPK not installed')

    def test_nationalscale_clustered_example_storage_inter_cluster(self):
        self.example_tester_storage_inter_cluster()

    def test_storage_inter_cluster_cyclic(self):
        model = self.model_runner(storage_inter_cluster=True, cyclic=True)
        # Full 1-hourly model run: 22312488.670967
        assert float(model.results.cost.sum()) == approx(18904055.722)

        # Full 1-hourly model run: 0.296973
        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.122564, abs=0.000001)

        # Full 1-hourly model run: 0.064362
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.075145, abs=0.000001)

    def test_storage_inter_cluster_no_storage(self):
        with pytest.warns(calliope.exceptions.ModelWarning) as excinfo:
            self.model_runner(storage_inter_cluster=True, storage=False)

        expected_warnings = [
            'Tech battery was removed by setting ``exists: False``',
            'Tech csp was removed by setting ``exists: False``'
        ]
        assert check_error_or_warning(excinfo, expected_warnings)


class TestUrbanScaleExampleModelSenseChecks:
    def example_tester(self, resource_unit, solver='cbc', solver_io=None):
        unit_override = {
            'techs.pv.constraints': {
                'resource': 'file=pv_resource.csv:{}'.format(resource_unit),
                'resource_unit': 'energy_{}'.format(resource_unit)
            },
            'run.solver': solver
        }
        override = {'model.subset_time': '2005-07-01', **unit_override}

        if solver_io:
            override['run.solver_io'] = solver_io

        model = calliope.examples.urban_scale(override_dict=override)
        model.run()

        assert model.results.energy_cap.to_pandas()['X1::chp'] == approx(250.090112)

        # GLPK isn't able to get the same answer both times, so we have to account for that here
        if resource_unit == 'per_cap' and solver == 'glpk':
            heat_pipe_approx = 183.45825
        else:
            heat_pipe_approx = 182.19260

        assert model.results.energy_cap.to_pandas()['X2::heat_pipes:N1'] == approx(heat_pipe_approx)

        assert model.results.carrier_prod.sum('timesteps').to_pandas()['X3::boiler::heat'] == approx(0.18720)
        assert model.results.resource_area.to_pandas()['X2::pv'] == approx(830.064659)

        assert float(model.results.carrier_export.sum()) == approx(122.7156)

        # GLPK doesn't agree with commercial solvers, so we have to account for that here
        cost_sum = 430.097399 if solver == 'glpk' else 430.089188
        assert float(model.results.cost.sum()) == approx(cost_sum)

    def test_urban_example_results_area(self):
        self.example_tester('per_area')

    def test_urban_example_results_area_gurobi(self):
        try:
            import gurobipy  # pylint: disable=unused-import
            self.example_tester('per_area', solver='gurobi', solver_io='python')
        except ImportError:
            pytest.skip('Gurobi not installed')

    def test_urban_example_results_cap(self):
        self.example_tester('per_cap')

    def test_urban_example_results_cap_gurobi(self):
        try:
            import gurobipy  # pylint: disable=unused-import
            self.example_tester('per_cap', solver='gurobi', solver_io='python')
        except ImportError:
            pytest.skip('Gurobi not installed')

    @pytest.mark.filterwarnings("ignore:(?s).*Integer:calliope.exceptions.ModelWarning")
    def test_milp_example_results(self):
        model = calliope.examples.milp(
            override_dict={'model.subset_time': '2005-01-01', 'run.solver_options.mipgap': 0.001}
        )
        model.run()

        assert model.results.energy_cap.to_pandas()['X1::chp'] == 300
        assert model.results.energy_cap.to_pandas()['X2::heat_pipes:N1'] == approx(188.363137)

        assert model.results.carrier_prod.sum('timesteps').to_pandas()['X1::supply_gas::gas'] == approx(12363.173036)
        assert float(model.results.carrier_export.sum()) == approx(0)

        assert model.results.purchased.to_pandas()['X2::boiler'] == 1
        assert model.results.units.to_pandas()['X1::chp'] == 1

        assert float(model.results.operating_units.sum()) == 24

        assert float(model.results.cost.sum()) == approx(540.780779)

    def test_operate_example_results(self):
        model = calliope.examples.operate(
            override_dict={'model.subset_time': ['2005-07-01', '2005-07-04']}
        )
        with pytest.warns(calliope.exceptions.ModelWarning) as excinfo:
            model.run()

        expected_warnings = [
            'Energy capacity constraint removed',
            'Resource capacity constraint defined and set to infinity for all supply_plus techs'
        ]

        assert check_error_or_warning(excinfo, expected_warnings)

        assert all(model.results.timesteps == pd.date_range('2005-07', '2005-07-04 23:00:00', freq='H'))

import shutil

import pytest
from pytest import approx
import pandas as pd

import calliope
from calliope.test.common.util import check_error_or_warning


class TestModelPreproccesing:
    def test_preprocess_national_scale(self):
        calliope.examples.national_scale()

    def test_preprocess_time_clustering(self):
        calliope.examples.time_clustering()

    def test_preprocess_time_resampling(self):
        calliope.examples.time_resampling()

    def test_preprocess_urban_scale(self):
        calliope.examples.urban_scale()

    @pytest.mark.filterwarnings("ignore:(?s).*Integer:calliope.exceptions.ModelWarning")
    def test_preprocess_milp(self):
        calliope.examples.milp()

    def test_preprocess_operate(self):
        calliope.examples.operate()

    def test_preprocess_time_masking(self):
        calliope.examples.time_masking()


class TestNationalScaleExampleModelSenseChecks:
    def example_tester(self, solver='cbc', solver_io=None):
        override = {
            'model.subset_time': '2005-01-01',
            'run.solver': solver,
        }

        if solver_io:
            override['run.solver_io'] = solver_io

        model = calliope.examples.national_scale(override_dict=override)
        model.run()

        assert model.results.storage_cap.to_pandas()['region1-1::csp'] == approx(45129.950)
        assert model.results.storage_cap.to_pandas()['region2::battery'] == approx(6675.173)

        assert model.results.energy_cap.to_pandas()['region1-1::csp'] == approx(4626.588)
        assert model.results.energy_cap.to_pandas()['region2::battery'] == approx(1000)
        assert model.results.energy_cap.to_pandas()['region1::ccgt'] == approx(30000)

        assert float(model.results.cost.sum()) == approx(38988.7442)

        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.063543, abs=0.000001)
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.2642256, abs=0.000001)

    def test_nationalscale_example_results_cbc(self):
        self.example_tester()

    def test_nationalscale_example_results_gurobi(self):
        try:
            import gurobipy  # pylint: disable=unused-import
            self.example_tester(solver='gurobi', solver_io='python')
        except ImportError:
            pytest.skip('Gurobi not installed')

    def test_nationalscale_example_results_cplex(self):
        if shutil.which('cplex'):
            self.example_tester(solver='cplex')
        else:
            pytest.skip('CPLEX not installed')

    def test_nationalscale_example_results_glpk(self):
        if shutil.which('glpsol'):
            self.example_tester(solver='glpk')
        else:
            pytest.skip('GLPK not installed')

    def test_considers_supply_generation_only_in_total_levelised_cost(self):
        # calculation of expected value:
        # costs = model.get_formatted_array("cost").sum(dim="locs")
        # gen = model.get_formatted_array("carrier_prod").sum(dim=["timesteps", "locs"])
        # lcoe = costs.sum(dim="techs") / gen.sel(techs=["ccgt", "csp"]).sum(dim="techs")
        model = calliope.examples.national_scale()
        model.run()

        assert model.results.total_levelised_cost.item() == approx(0.067005, abs=1e-5)

    def test_fails_gracefully_without_timeseries(self):
        override = {
            "locations.region1.techs.demand_power.constraints.resource": -200,
            "locations.region2.techs.demand_power.constraints.resource": -400,
            "techs.csp.constraints.resource": 100
        }
        with pytest.raises(calliope.exceptions.ModelError):
            calliope.examples.national_scale(override_dict=override)


class TestNationalScaleExampleModelInfeasibility:
    def example_tester(self):

        model = calliope.examples.national_scale(
            scenario='check_feasibility',
            override_dict={'run.cyclic_storage': False}
        )

        model.run()

        assert model.results.attrs['termination_condition'] in ['infeasible', 'other']  # glpk gives 'other' as result

        assert 'systemwide_levelised_cost' not in model.results.data_vars
        assert 'systemwide_capacity_factor' not in model.results.data_vars

    def test_nationalscale_example_results_cbc(self):
        self.example_tester()


class TestNationalScaleExampleModelOperate:
    def example_tester(self):
        with pytest.warns(calliope.exceptions.ModelWarning) as excinfo:
            model = calliope.examples.national_scale(
                override_dict={'model.subset_time': ['2005-01-01', '2005-01-03']},
                scenario='operate')
            model.run()

        expected_warnings = [
            'Energy capacity constraint removed from region1::demand_power as force_resource is applied',
            'Energy capacity constraint removed from region2::demand_power as force_resource is applied',
            'Resource capacity constraint defined and set to infinity for all supply_plus techs'
        ]

        assert check_error_or_warning(excinfo, expected_warnings)
        assert all(model.results.timesteps == pd.date_range('2005-01', '2005-01-03 23:00:00', freq='H'))

    def test_nationalscale_example_results_cbc(self):
        self.example_tester()


class TestNationalScaleResampledExampleModelSenseChecks:
    def example_tester(self, solver='cbc', solver_io=None):
        override = {
            'model.subset_time': '2005-01-01',
            'run.solver': solver,
        }

        if solver_io:
            override['run.solver_io'] = solver_io

        model = calliope.examples.time_resampling(override_dict=override)
        model.run()

        assert model.results.storage_cap.to_pandas()['region1-1::csp'] == approx(23563.444)
        assert model.results.storage_cap.to_pandas()['region2::battery'] == approx(6315.78947)

        assert model.results.energy_cap.to_pandas()['region1-1::csp'] == approx(1440.8377)
        assert model.results.energy_cap.to_pandas()['region2::battery'] == approx(1000)
        assert model.results.energy_cap.to_pandas()['region1::ccgt'] == approx(30000)

        assert float(model.results.cost.sum()) == approx(37344.221869)

        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.063543, abs=0.000001)
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.25, abs=0.000001)

    def test_nationalscale_resampled_example_results_cbc(self):
        self.example_tester()

    def test_nationalscale_resampled_example_results_glpk(self):
        if shutil.which('glpsol'):
            self.example_tester(solver='glpk')
        else:
            pytest.skip('GLPK not installed')


class TestNationalScaleClusteredExampleModelSenseChecks:
    def model_runner(self, solver='cbc', solver_io=None,
                     how='closest', storage_inter_cluster=False,
                     cyclic=False, storage=True):
        override = {
            'model.time.function_options': {
                'how': how, 'storage_inter_cluster': storage_inter_cluster
            },
            'run.solver': solver,
            'run.cyclic_storage': cyclic
        }
        if storage is False:
            override.update({
                'techs.battery.exists': False,
                'techs.csp.exists': False
            })

        if solver_io:
            override['run.solver_io'] = solver_io

        model = calliope.examples.time_clustering(override_dict=override)
        model.run()

        return model

    def example_tester_closest(self, solver='cbc', solver_io=None):
        model = self.model_runner(solver=solver, solver_io=solver_io, how='closest')
        # Full 1-hourly model run: 22312488.670967
        assert float(model.results.cost.sum()) == approx(49670627.15297682)

        # Full 1-hourly model run: 0.296973
        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.137105, abs=0.000001)

        # Full 1-hourly model run: 0.064362
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.064501, abs=0.000001)

    def example_tester_mean(self, solver='cbc', solver_io=None):
        model = self.model_runner(solver=solver, solver_io=solver_io, how='mean')
        # Full 1-hourly model run: 22312488.670967
        assert float(model.results.cost.sum()) == approx(22172253.328)

        # Full 1-hourly model run: 0.296973
        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.127783, abs=0.000001)

        # Full 1-hourly model run: 0.064362
        assert float(
            model.results.systemwide_capacity_factor.loc[dict(carriers='power')].to_pandas().T['battery']
        ) == approx(0.044458, abs=0.000001)

    def example_tester_storage_inter_cluster(self):
        model = self.model_runner(storage_inter_cluster=True)

        # Full 1-hourly model run: 22312488.670967
        assert float(model.results.cost.sum()) == approx(21825515.304)

        # Full 1-hourly model run: 0.296973
        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.100760, abs=0.000001)

        # Full 1-hourly model run: 0.064362
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.091036, abs=0.000001)

    def test_nationalscale_clustered_example_closest_results_cbc(self):
        self.example_tester_closest()

    def test_nationalscale_clustered_example_closest_results_glpk(self):
        if shutil.which('glpsol'):
            self.example_tester_closest(solver='glpk')
        else:
            pytest.skip('GLPK not installed')

    def test_nationalscale_clustered_example_mean_results_cbc(self):
        self.example_tester_mean()

    @pytest.mark.skip(reason='GLPK is useless and delivering different results on different operating systems')
    def test_nationalscale_clustered_example_mean_results_glpk(self):
        if shutil.which('glpsol'):
            self.example_tester_mean(solver='glpk')
        else:
            pytest.skip('GLPK not installed')

    def test_nationalscale_clustered_example_storage_inter_cluster(self):
        self.example_tester_storage_inter_cluster()

    def test_storage_inter_cluster_cyclic(self):
        model = self.model_runner(storage_inter_cluster=True, cyclic=True)
        # Full 1-hourly model run: 22312488.670967
        assert float(model.results.cost.sum()) == approx(18904055.722)

        # Full 1-hourly model run: 0.296973
        assert float(
            model.results.systemwide_levelised_cost.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.122564, abs=0.000001)

        # Full 1-hourly model run: 0.064362
        assert float(
            model.results.systemwide_capacity_factor.loc[{'carriers': 'power', 'techs': 'battery'}].item()
        ) == approx(0.075145, abs=0.000001)

    def test_storage_inter_cluster_no_storage(self):
        with pytest.warns(calliope.exceptions.ModelWarning) as excinfo:
            self.model_runner(storage_inter_cluster=True, storage=False)

        expected_warnings = [
            'Tech battery was removed by setting ``exists: False``',
            'Tech csp was removed by setting ``exists: False``'
        ]
        assert check_error_or_warning(excinfo, expected_warnings)


class TestUrbanScaleExampleModelSenseChecks:
    def example_tester(self, resource_unit, solver='cbc', solver_io=None):
        unit_override = {
            'techs.pv.constraints': {
                'resource': 'file=pv_resource.csv:{}'.format(resource_unit),
                'resource_unit': 'energy_{}'.format(resource_unit)
            },
            'run.solver': solver
        }
        override = {'model.subset_time': '2005-07-01', **unit_override}

        if solver_io:
            override['run.solver_io'] = solver_io

        model = calliope.examples.urban_scale(override_dict=override)
        model.run()

        assert model.results.energy_cap.to_pandas()['X1::chp'] == approx(250.090112)

        # GLPK isn't able to get the same answer both times, so we have to account for that here
        if resource_unit == 'per_cap' and solver == 'glpk':
            heat_pipe_approx = 183.45825
        else:
            heat_pipe_approx = 182.19260

        assert model.results.energy_cap.to_pandas()['X2::heat_pipes:N1'] == approx(heat_pipe_approx)

        assert model.results.carrier_prod.sum('timesteps').to_pandas()['X3::boiler::heat'] == approx(0.18720)
        assert model.results.resource_area.to_pandas()['X2::pv'] == approx(830.064659)

        assert float(model.results.carrier_export.sum()) == approx(122.7156)

        # GLPK doesn't agree with commercial solvers, so we have to account for that here
        cost_sum = 430.097399 if solver == 'glpk' else 430.089188
        assert float(model.results.cost.sum()) == approx(cost_sum)

    def test_urban_example_results_area(self):
        self.example_tester('per_area')

    def test_urban_example_results_area_gurobi(self):
        try:
            import gurobipy  # pylint: disable=unused-import
            self.example_tester('per_area', solver='gurobi', solver_io='python')
        except ImportError:
            pytest.skip('Gurobi not installed')

    def test_urban_example_results_cap(self):
        self.example_tester('per_cap')

    def test_urban_example_results_cap_gurobi(self):
        try:
            import gurobipy  # pylint: disable=unused-import
            self.example_tester('per_cap', solver='gurobi', solver_io='python')
        except ImportError:
            pytest.skip('Gurobi not installed')

    @pytest.mark.filterwarnings("ignore:(?s).*Integer:calliope.exceptions.ModelWarning")
    def test_milp_example_results(self):
        model = calliope.examples.milp(
            override_dict={'model.subset_time': '2005-01-01', 'run.solver_options.mipgap': 0.001}
        )
        model.run()

        assert model.results.energy_cap.to_pandas()['X1::chp'] == 300
        assert model.results.energy_cap.to_pandas()['X2::heat_pipes:N1'] == approx(188.363137)

        assert model.results.carrier_prod.sum('timesteps').to_pandas()['X1::supply_gas::gas'] == approx(12363.173036)
        assert float(model.results.carrier_export.sum()) == approx(0)

        assert model.results.purchased.to_pandas()['X2::boiler'] == 1
        assert model.results.units.to_pandas()['X1::chp'] == 1

        assert float(model.results.operating_units.sum()) == 24

        assert float(model.results.cost.sum()) == approx(540.780779)

    def test_operate_example_results(self):
        model = calliope.examples.operate(
            override_dict={'model.subset_time': ['2005-07-01', '2005-07-04']}
        )
        with pytest.warns(calliope.exceptions.ModelWarning) as excinfo:
            model.run()

        expected_warnings = [
            'Energy capacity constraint removed',
            'Resource capacity constraint defined and set to infinity for all supply_plus techs'
        ]

        assert check_error_or_warning(excinfo, expected_warnings)

        assert all(model.results.timesteps == pd.date_range('2005-07', '2005-07-04 23:00:00', freq='H'))

# pylint: disable=unused-import # calculation of expected value: # costs = model.get_formatted_array("cost").sum(dim="locs") # gen = model.get_formatted_array("carrier_prod").sum(dim=["timesteps", "locs"]) # lcoe = costs.sum(dim="techs") / gen.sel(techs=["ccgt", "csp"]).sum(dim="techs") # glpk gives 'other' as result # Full 1-hourly model run: 22312488.670967 # Full 1-hourly model run: 0.296973 # Full 1-hourly model run: 0.064362 # Full 1-hourly model run: 22312488.670967 # Full 1-hourly model run: 0.296973 # Full 1-hourly model run: 0.064362 # Full 1-hourly model run: 22312488.670967 # Full 1-hourly model run: 0.296973 # Full 1-hourly model run: 0.064362 # Full 1-hourly model run: 22312488.670967 # Full 1-hourly model run: 0.296973 # Full 1-hourly model run: 0.064362 # GLPK isn't able to get the same answer both times, so we have to account for that here # GLPK doesn't agree with commercial solvers, so we have to account for that here # pylint: disable=unused-import # pylint: disable=unused-import