import json
import os
import tempfile
from datetime import datetime
from pathlib import Path
import pytest

# Wir importieren aus dem Projektpaket artifact_001.core
import importlib.util

def load_log_measurement():
    # Dynamisches Laden der log_measurement Funktion aus src/artifact_001/core.py
    # Dies ist CI-safe, da ansonsten ImportError entstehen könnte, falls sys.path anders konfiguriert ist.
    pkg_path = Path(__file__).parent.parent / 'src' / 'artifact_001' / 'core.py'
    spec = importlib.util.spec_from_file_location('artifact_001.core', pkg_path)
    mod = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(mod)
    return mod.log_measurement

@pytest.fixture
def sample_measurement():
    return {
        'timestamp': datetime(2024, 6, 10, 7, 5, 0),
        'measured_p': 0.134,
        'freeze_ok': False,
        'setup_fingerprint': 'setupA',
        'policy_hash': 'ph123'
    }

@pytest.fixture(autouse=True)
def temp_output_dir(monkeypatch, tmp_path):
    output_path = tmp_path / 'measurement_log.json'
    os.makedirs(tmp_path, exist_ok=True)
    # Patch the output path in core.py to use this temp file.
    monkeypatch.setenv('AUX3_OUTPUT_PATH', str(output_path))
    yield output_path

@pytest.mark.parametrize('invalid_case', [
    {'measured_p': 'no_float'},  # falscher Typ
    {'freeze_ok': 'not_bool'},  # falscher Typ
    {'timestamp': 'invalid-date'},  # ungültiges Datum
])
def test_log_measurement_invalid_input(invalid_case, sample_measurement, temp_output_dir):
    log_measurement = load_log_measurement()
    bad_input = sample_measurement.copy()
    bad_input.update(invalid_case)
    with pytest.raises(Exception):
        log_measurement(**bad_input)


def test_log_measurement_creates_valid_json(sample_measurement, temp_output_dir):
    log_measurement = load_log_measurement()
    fpath = temp_output_dir
    # Sicherstellen, dass Datei leer ist
    if fpath.exists():
        fpath.unlink()

    # Aufruf der Funktion
    log_measurement(**sample_measurement)

    assert fpath.exists(), 'JSON-Logdatei sollte erstellt werden.'

    content = json.loads(fpath.read_text(encoding='utf-8'))

    assert isinstance(content, list), 'JSON-Datei sollte eine Liste enthalten.'
    assert len(content) == 1, 'Ein Messpunkt sollte geloggt sein.'
    entry = content[0]

    # Strukturprüfung basierend auf MeasurementLog data_model
    expected_fields = ['timestamp', 'measured_p', 'freeze_ok', 'setup_fingerprint', 'policy_hash']
    for field in expected_fields:
        assert field in entry, f'Feld {field} sollte vorhanden sein.'

    # Typ-Assertions (Nominal Case)
    datetime.fromisoformat(entry['timestamp'])
    assert isinstance(entry['measured_p'], float)
    assert isinstance(entry['freeze_ok'], bool)
    assert isinstance(entry['setup_fingerprint'], str)
    assert isinstance(entry['policy_hash'], str)


def test_log_measurement_appends_to_existing(temp_output_dir, sample_measurement):
    log_measurement = load_log_measurement()
    fpath = temp_output_dir

    # Erstes Logging
    log_measurement(**sample_measurement)

    # Zweites Logging – sollte anhängen
    modified = sample_measurement.copy()
    modified['measured_p'] = 0.555
    log_measurement(**modified)

    content = json.loads(fpath.read_text(encoding='utf-8'))
    assert len(content) == 2, 'Zwei Messpunkte sollten geloggt sein.'

    # Letzter Eintrag überprüfen
    assert pytest.approx(content[-1]['measured_p'], 1e-6) == 0.555