/*
 * RiskScape™ Copyright New Zealand Institute for Earth Science Limited
 * (Earth Sciences New Zealand) is distributed for research purposes only
 * under the terms of AGPLv3.
 *
 * RiskScape™ Copyright 2026 New Zealand Institute for Earth Science
 * Limited (Earth Sciences New Zealand). All rights reserved. Source code
 * available under the AGPLv3.
 * 
 * This program is free software: you can redistribute it and/or modify it under
 *  the terms of the GNU Affero 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 for RESEARCH PURPOSES ONLY, in the hope that it will
 * be useful for research and education initiatives.
 * 
 * If you are not a researcher, or you are a researcher who wishes to use this
 * program on terms other than AGPLv3 (including those who wish to restrict the
 * distribution of any source code created using this program), please contact:
 * https://riskscape.org.nz
 * 
 * This program is distributed WITHOUT ANY WARRANTY; without even the implied
 * warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Affero General Public License for more details.  You should have received a copy
 * of the GNU Affero General Public License along with this program.  If not, see
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 * 
 * By way of summary only, under the AGPLv3:
 *     • Permissions of this strongest copyleft license are conditioned
 *       on making available complete source code of licensed works and
 *       modifications, which include larger works using a licensed work,
 *       under the same license.
 *     • Copyright and license notices must be preserved.
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 */
package nz.org.riskscape.engine.cli.tests;

import static org.hamcrest.Matchers.*;
import static org.junit.Assert.*;

import nz.org.riskscape.engine.test.EngineTestPlugins;
import nz.org.riskscape.engine.test.EngineTestSettings;
import nz.org.riskscape.test.TestUsesRemoteData;
import org.junit.Test;
import org.junit.experimental.categories.Category;

import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Arrays;

/**
 * Spot-checks the exemplar DEVORA case study
 */
@Category(TestUsesRemoteData.class)
@EngineTestPlugins({"defaults", "beta", "cpython"})
@EngineTestSettings({"cpython.python3-bin=python3", "cpython.lib-dir=lib"})
public class DevoraCaseStudyTest extends BaseExemplarTest {

  public Path stdhome() {
    return Paths.get("..", "..", "exemplars", "case-studies", "DEVORA");
  }

  @Test
  public void canRunDevoraSubstationModel() throws Exception {
    runCommand.modelId = "Substation-Impact";
    runCommand.run();

    // note that models use randomness, but these 4 substations are exposed to PDC so should always require replacement
    assertThat(openCsv("event-impact.csv", "time_sequence", "timeline", "description",
            "No_Impact.Total.Count", "Cleaning_Required.Total.Count", "Repair_Required.Total.Count",
            "Replacement_Or_Expensive_Repair.Total.Count"), hasItem(
                    is(Arrays.asList("5", "April 3rd-14th", "Lava flows", "16", "", "", "4"))
            )
    );
  }

  @Test
  public void canRunDevoraTransmissionLinesModel() throws Exception {
    runCommand.modelId = "Transmission-Line-Impact";
    runCommand.run();

    // note that models use randomness, so just check it runs and we get 5 rows of output
    assertThat(openCsv("event-impact.csv", "time_sequence", "timeline", "description",
            "No_Impact.Total.Count", "Cleaning_Required.Total.Count", "Repair_Required.Total.Count",
            "Replacement_Or_Expensive_Repair.Total.Count").size(), is(5)
    );
  }

  @Test
  public void canRunDevoraRailModelWithSegmenting() throws Exception {
    // rail model relies on having a LINZ secret setup
    addKoordinatesSecret("linz", "data.linz.govt.nz", getApiKey(LINZ_KEY));

    runCommand.modelId = "Rail-Impact";
    // test segmenting the asset this time
    runCommand.parameters = Arrays.asList("segment_m=100");
    runCommand.run();

    // note that models use randomness, so just check it runs and we get 5 rows of output
    assertThat(openCsv("event-impact.csv", "time_sequence", "timeline", "description", "No_Disruption.Total.Segments",
            "No_Disruption.Total.Impacted_Km", "Speed_Reductions_Timetable_Alterations.Total.Segments",
            "Speed_Reductions_Timetable_Alterations.Total.Impacted_Km", "Closure_Possible.Total.Segments",
            "Closure_Possible.Total.Impacted_Km", "Closure_Likely.Total.Segments",
            "Closure_Likely.Total.Impacted_Km").size(), is(5)
    );
    removeSecret("linz");
  }

  // NB: I've opted not to run the Road-Impact model here as it uses the same pipeline as
  // the other models, but it's a bit slower to fetch the LINZ road dataset
}
