import {
  parseCSVHeaders,
  parseCSV,
  getUniqueNoteValues,
  runAnalysis,
} from '../src/lib/csvAnalysis';

// ── Fixtures ───────────────────────────────────────────────────────────────────

// Minimal CSV mimicking the real experiment file structure
const FIXTURE_CSV = `timestamp,frame_number,frame_line_status,note
0.0,1,10,
1.0,2,10,f
2.0,3,10,s
3.0,4,10,s
4.0,5,10,f
5.0,6,10,
6.0,7,10,s
7.0,8,10,f
8.0,9,10,f
9.0,10,10,s`;

// Same data but with Windows-style line endings
const FIXTURE_CSV_CRLF = FIXTURE_CSV.replace(/\n/g, '\r\n');

// CSV with quoted field containing a comma
const FIXTURE_CSV_QUOTED = `timestamp,note\n1.0,"hello, world"\n2.0,plain`;

// Out-of-order timestamps (should be sorted)
const FIXTURE_UNSORTED = `timestamp,note
3.0,s
1.0,f
2.0,s`;

const CLASSIFICATIONS = { f: 'Failure', s: 'Success' };

// ── parseCSVHeaders ────────────────────────────────────────────────────────────

describe('parseCSVHeaders', () => {
  test('returns column names from first line', () => {
    expect(parseCSVHeaders(FIXTURE_CSV)).toEqual([
      'timestamp', 'frame_number', 'frame_line_status', 'note',
    ]);
  });

  test('handles CRLF line endings', () => {
    expect(parseCSVHeaders(FIXTURE_CSV_CRLF)).toEqual([
      'timestamp', 'frame_number', 'frame_line_status', 'note',
    ]);
  });

  test('handles single-line CSV (no newline)', () => {
    expect(parseCSVHeaders('a,b,c')).toEqual(['a', 'b', 'c']);
  });
});

// ── parseCSV ──────────────────────────────────────────────────────────────────

describe('parseCSV', () => {
  test('returns headers and rows', () => {
    const { headers, rows } = parseCSV(FIXTURE_CSV);
    expect(headers).toEqual(['timestamp', 'frame_number', 'frame_line_status', 'note']);
    expect(rows).toHaveLength(10);
  });

  test('row values are keyed by header name', () => {
    const { rows } = parseCSV(FIXTURE_CSV);
    expect(rows[0]).toEqual({ timestamp: '0.0', frame_number: '1', frame_line_status: '10', note: '' });
    expect(rows[1]).toEqual({ timestamp: '1.0', frame_number: '2', frame_line_status: '10', note: 'f' });
  });

  test('handles CRLF line endings', () => {
    const { rows } = parseCSV(FIXTURE_CSV_CRLF);
    expect(rows).toHaveLength(10);
    expect(rows[1].note).toBe('f');
  });

  test('handles quoted fields containing commas', () => {
    const { rows } = parseCSV(FIXTURE_CSV_QUOTED);
    expect(rows[0].note).toBe('hello, world');
    expect(rows[1].note).toBe('plain');
  });

  test('skips blank lines', () => {
    const csv = 'a,b\n1,2\n\n3,4\n';
    const { rows } = parseCSV(csv);
    expect(rows).toHaveLength(2);
  });
});

// ── getUniqueNoteValues ────────────────────────────────────────────────────────

describe('getUniqueNoteValues', () => {
  test('returns sorted unique non-empty values', () => {
    const { rows } = parseCSV(FIXTURE_CSV);
    expect(getUniqueNoteValues(rows, 'note')).toEqual(['f', 's']);
  });

  test('ignores empty strings', () => {
    const rows = [{ note: '' }, { note: 'a' }, { note: '' }, { note: 'b' }];
    expect(getUniqueNoteValues(rows, 'note')).toEqual(['a', 'b']);
  });

  test('trims whitespace before deduplication', () => {
    const rows = [{ note: ' a ' }, { note: 'a' }, { note: 'b' }];
    expect(getUniqueNoteValues(rows, 'note')).toEqual(['a', 'b']);
  });

  test('returns empty array when no non-empty notes', () => {
    const rows = [{ note: '' }, { note: '' }];
    expect(getUniqueNoteValues(rows, 'note')).toEqual([]);
  });
});

// ── runAnalysis ────────────────────────────────────────────────────────────────

describe('runAnalysis', () => {
  let result;

  beforeEach(() => {
    const { rows } = parseCSV(FIXTURE_CSV);
    result = runAnalysis(rows, 'timestamp', 'note', CLASSIFICATIONS);
  });

  // Fixture note sequence (sorted by timestamp, empty rows excluded):
  // t=1 f, t=2 s, t=3 s, t=4 f, t=6 s, t=7 f, t=8 f, t=9 s
  // Categories: F S S F S F F S

  test('totalNoteRows counts only rows with non-empty note', () => {
    expect(result.totalNoteRows).toBe(8);
  });

  test('sequence preserves order and maps to categories', () => {
    const notes = result.sequence.map(r => r.note);
    expect(notes).toEqual(['f', 's', 's', 'f', 's', 'f', 'f', 's']);

    const cats = result.sequence.map(r => r.category);
    expect(cats).toEqual(['Failure', 'Success', 'Success', 'Failure', 'Success', 'Failure', 'Failure', 'Success']);
  });

  test('categoryCounts totals are correct', () => {
    expect(result.categoryCounts['Failure'].total).toBe(4);
    expect(result.categoryCounts['Success'].total).toBe(4);
  });

  test('categoryCounts byNote breakdown is correct', () => {
    expect(result.categoryCounts['Failure'].byNote).toEqual({ f: 4 });
    expect(result.categoryCounts['Success'].byNote).toEqual({ s: 4 });
  });

  test('runs (run-length encoding) are correct', () => {
    // F S S F S F F S  →  F×1, S×2, F×1, S×1, F×2, S×1
    expect(result.runs).toEqual([
      { category: 'Failure', length: 1 },
      { category: 'Success', length: 2 },
      { category: 'Failure', length: 1 },
      { category: 'Success', length: 1 },
      { category: 'Failure', length: 2 },
      { category: 'Success', length: 1 },
    ]);
  });

  test('consecutiveStats totalRuns', () => {
    expect(result.consecutiveStats['Failure'].totalRuns).toBe(3);
    expect(result.consecutiveStats['Success'].totalRuns).toBe(3);
  });

  test('consecutiveStats maxRun', () => {
    expect(result.consecutiveStats['Failure'].maxRun).toBe(2);
    expect(result.consecutiveStats['Success'].maxRun).toBe(2);
  });

  test('consecutiveStats avgRun', () => {
    // Failure runs: [1,1,2] → avg = 4/3
    expect(result.consecutiveStats['Failure'].avgRun).toBeCloseTo(4 / 3);
    // Success runs: [2,1,1] → avg = 4/3
    expect(result.consecutiveStats['Success'].avgRun).toBeCloseTo(4 / 3);
  });

  test('consecutiveStats runLengths array', () => {
    expect(result.consecutiveStats['Failure'].runLengths).toEqual([1, 1, 2]);
    expect(result.consecutiveStats['Success'].runLengths).toEqual([2, 1, 1]);
  });
});

describe('runAnalysis — sorting', () => {
  test('sorts rows by timestamp before building sequence', () => {
    const { rows } = parseCSV(FIXTURE_UNSORTED);
    // Input order: s(3), f(1), s(2) — sorted order: f(1), s(2), s(3)
    const result = runAnalysis(rows, 'timestamp', 'note', CLASSIFICATIONS);
    expect(result.sequence.map(r => r.note)).toEqual(['f', 's', 's']);
  });
});

describe('runAnalysis — unclassified notes', () => {
  test('unclassified notes appear in sequence with null category', () => {
    const { rows } = parseCSV(FIXTURE_CSV);
    // Only classify 'f'; leave 's' unclassified
    const result = runAnalysis(rows, 'timestamp', 'note', { f: 'Failure' });
    const nullCats = result.sequence.filter(r => r.category === null);
    expect(nullCats.length).toBe(4); // 4 's' notes
  });

  test('unclassified notes are excluded from categoryCounts', () => {
    const { rows } = parseCSV(FIXTURE_CSV);
    const result = runAnalysis(rows, 'timestamp', 'note', { f: 'Failure' });
    expect(result.categoryCounts['Success']).toBeUndefined();
  });

  test('unclassified notes do not break or extend runs', () => {
    // Sequence with 'x' unclassified: f x f → should still be one Failure run of 2
    const csv = 'ts,note\n1.0,f\n2.0,x\n3.0,f';
    const { rows } = parseCSV(csv);
    // 'x' is unclassified, 'f' → Failure
    // After skipping 'x': f, f → one run of Failure×2
    const result = runAnalysis(rows, 'ts', 'note', { f: 'Failure' });
    expect(result.runs).toEqual([{ category: 'Failure', length: 2 }]);
  });
});

describe('runAnalysis — multiple note values per category', () => {
  test('groups multiple note values under one category', () => {
    // The user's example: ss and s both belong to Success
    const csv = 'ts,note\n1.0,ss\n2.0,s\n3.0,s';
    const { rows } = parseCSV(csv);
    const result = runAnalysis(rows, 'ts', 'note', { ss: 'Success', s: 'Success' });
    expect(result.categoryCounts['Success'].total).toBe(3);
    expect(result.categoryCounts['Success'].byNote).toEqual({ ss: 1, s: 2 });
  });

  test('run-length encoding with multiple note values in same category', () => {
    const csv = 'ts,note\n1.0,ss\n2.0,s\n3.0,f';
    const { rows } = parseCSV(csv);
    const result = runAnalysis(rows, 'ts', 'note', { ss: 'Success', s: 'Success', f: 'Failure' });
    // ss and s are both Success → run of 2, then Failure run of 1
    expect(result.runs).toEqual([
      { category: 'Success', length: 2 },
      { category: 'Failure', length: 1 },
    ]);
  });
});

describe('runAnalysis — edge cases', () => {
  test('empty rows returns zero totals', () => {
    const result = runAnalysis([], 'timestamp', 'note', CLASSIFICATIONS);
    expect(result.totalNoteRows).toBe(0);
    expect(result.sequence).toEqual([]);
    expect(result.runs).toEqual([]);
    expect(result.categoryCounts).toEqual({});
    expect(result.consecutiveStats).toEqual({});
  });

  test('all notes unclassified', () => {
    const { rows } = parseCSV(FIXTURE_CSV);
    const result = runAnalysis(rows, 'timestamp', 'note', {});
    expect(result.categoryCounts).toEqual({});
    expect(result.runs).toEqual([]);
  });

  test('single note row', () => {
    const csv = 'ts,note\n1.0,f';
    const { rows } = parseCSV(csv);
    const result = runAnalysis(rows, 'ts', 'note', { f: 'Failure' });
    expect(result.totalNoteRows).toBe(1);
    expect(result.runs).toEqual([{ category: 'Failure', length: 1 }]);
    expect(result.consecutiveStats['Failure'].maxRun).toBe(1);
    expect(result.consecutiveStats['Failure'].avgRun).toBe(1);
  });

  test('all same category', () => {
    const csv = 'ts,note\n1.0,f\n2.0,f\n3.0,f';
    const { rows } = parseCSV(csv);
    const result = runAnalysis(rows, 'ts', 'note', { f: 'Failure' });
    expect(result.runs).toEqual([{ category: 'Failure', length: 3 }]);
    expect(result.consecutiveStats['Failure'].maxRun).toBe(3);
    expect(result.consecutiveStats['Failure'].totalRuns).toBe(1);
  });
});