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Author SHA1 Message Date
Johan LEROY 83caa9006e chore(frontend): active strict, ajoute .form-select et documente le spec ciblé
Frontend / Audit des dépendances (push) Successful in 5s
SonarQube / build-back (push) Successful in 1m6s
Frontend / build (push) Successful in 9m47s
SonarQube / test-ml (push) Failing after 2m2s
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SonarQube / test-back (push) Failing after 49s
Frontend / test (push) Failing after 5m5s
SonarQube / test-front (push) Failing after 5m3s
SonarQube / SonarQube (push) Skipped
- tsconfig.json : "strict": true, vérifié sans erreur sur app et specs
- _forms.scss : .form-select, select natif habillé comme .form-input avec un
  chevron, documenté dans le design système
- TESTING.md : commande pour jouer un seul fichier ou dossier de specs
2026-09-21 14:25:30 +02:00
Johan LEROY 91752a2b0b fix(frontend): aligne le modèle Alert et AlertsService sur le contrat /alerts
Le modèle front portait un alert_id texte, des value/threshold non nullables et
ignorait metric et prediction_id, alors que l'API sérialise un entier, des
flottants nullables et ces deux champs. Toute comparaison avec l'alert_id d'une
recommandation échouait silencieusement.

- alert.model.ts : alert_id number, value/threshold/metric/prediction_id nullables
- alerts.service.ts : getAlerts(filters) pose site_id et severity en HttpParams,
  les deux filtres que l'API accepte
- alerts.fixture.ts : réaligné sur le contrat (ids entiers, horodatages UTC,
  champs nuls sur outage/sensor, un cas spike)
- alert-presentation.ts : tons, libellés et unités partagés ; low passe en
  neutre, le vert se lisait comme un état sain
2026-09-21 14:25:30 +02:00
Dorian 3c378c177f ci(ml,etl): analyse ml/ et etl/airflow dans SonarCloud avec un rapport de couverture ML 2026-09-21 14:12:38 +02:00
Dorian 2adfdf0eb0 fix(backend): supprime les vulnerabilites Sonar du Dockerfile et allege les tests d'exception 2026-09-21 14:08:04 +02:00
PhyriosandGitHub 44f3416ffe Merge branch 'main' into dev 2026-09-21 13:48:50 +02:00
Johan LEROYandGitHub 342128ccff Merge pull request #121 from ineszang/docs/livrables-ec03-ec06
docs(architecture,ml): vue CI/CD, ML-START.md et SAST Bandit
2026-09-21 13:45:46 +02:00
PhyriosandGitHub c3fd9327ea Definition des jalons 2026-09-14 11:30:01 +02:00
26 changed files with 640 additions and 353 deletions
+38 -1
View File
@@ -5,11 +5,15 @@ on:
paths:
- "apps/frontend/**"
- "apps/backend/**"
- "ml/**"
- "etl/airflow/**"
- ".github/workflows/sonarqube.yml"
pull_request:
paths:
- "apps/frontend/**"
- "apps/backend/**"
- "ml/**"
- "etl/airflow/**"
- ".github/workflows/sonarqube.yml"
@@ -108,8 +112,36 @@ jobs:
name: backend-coverage
path: apps/backend/coverage.xml
test-ml:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- name: Installe uv
uses: astral-sh/setup-uv@v5
with:
enable-cache: true
cache-dependency-glob: ml/uv.lock
- name: Installe l'interpréteur déclaré par .python-version
run: uv python install
working-directory: ml
- name: Synchronise les dépendances sans dévier du verrou
run: uv sync --all-groups --frozen
working-directory: ml
- name: Lancement des tests et génération du rapport de couverture (ML)
run: uv run pytest --cov-report=xml
working-directory: ml
- name: Upload coverage
uses: actions/upload-artifact@v4
with:
name: ml-coverage
path: ml/coverage.xml
sonarqube:
needs: [build-front, build-back, test-front, test-back]
needs: [build-front, build-back, test-front, test-back, test-ml]
name: SonarQube
runs-on: ubuntu-latest
steps:
@@ -126,6 +158,11 @@ jobs:
with:
name: backend-coverage
path: apps/backend
- name: Téléchargement du rapport de couverture (ML)
uses: actions/download-artifact@v4
with:
name: ml-coverage
path: ml
- name: SonarQube Scan
uses: SonarSource/sonarqube-scan-action@v8
env:
+5 -4
View File
@@ -11,13 +11,14 @@ WORKDIR /app
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=uv.lock,target=uv.lock \
--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
uv sync --locked --no-install-project --no-dev
uv sync --locked --no-install-project --no-dev --no-build
# Le projet lui-meme n'est pas installe (pas de second `uv sync`) : il tourne depuis /app, le
# repertoire de travail, et rien ne lit ses metadonnees. L'installer imposerait de le construire
# (backend hatchling), donc de retirer `--no-build` de l'etape ci-dessus, qui garantit que
# l'installation des dependances n'execute aucun script de build (regle Sonar docker:S8541).
COPY . /app
RUN --mount=type=cache,target=/root/.cache/uv \
uv sync --locked --no-dev
FROM python:3.14-slim AS runtime
+29 -19
View File
@@ -112,8 +112,10 @@ async def test_duplicate_reading_is_rejected_when_key_matches(
)
await data_connection.execute(statement)
savepoint = data_connection.begin_nested()
with pytest.raises(IntegrityError):
async with data_connection.begin_nested():
async with savepoint:
await data_connection.execute(statement)
@@ -147,9 +149,12 @@ async def test_invalid_reading_is_rejected_when_constraints_fail(
}
values.update(changes)
statement = insert(Reading).values(**values)
savepoint = data_connection.begin_nested()
with pytest.raises(IntegrityError):
async with data_connection.begin_nested():
await data_connection.execute(insert(Reading).values(**values))
async with savepoint:
await data_connection.execute(statement)
async def test_prediction_requires_period_when_energy_is_predicted(
@@ -164,8 +169,10 @@ async def test_prediction_requires_period_when_energy_is_predicted(
model_reference="test-model/1",
)
savepoint = data_connection.begin_nested()
with pytest.raises(IntegrityError):
async with data_connection.begin_nested():
async with savepoint:
await data_connection.execute(statement)
@@ -212,21 +219,22 @@ async def test_alert_rejects_prediction_when_site_differs(
)
).scalar_one()
statement = insert(Alert).values(
source_alert_id=str(uuid4()),
site_id=other_site,
source="enervision",
timestamp=MOMENT,
type="spike",
severity="high",
message="Test",
prediction_id=prediction_id,
raw_data={},
)
savepoint = data_connection.begin_nested()
with pytest.raises(IntegrityError):
async with data_connection.begin_nested():
await data_connection.execute(
insert(Alert).values(
source_alert_id=str(uuid4()),
site_id=other_site,
source="enervision",
timestamp=MOMENT,
type="spike",
severity="high",
message="Test",
prediction_id=prediction_id,
raw_data={},
)
)
async with savepoint:
await data_connection.execute(statement)
async def test_recommendation_is_unique_when_alert_and_rule_match(
@@ -256,6 +264,8 @@ async def test_recommendation_is_unique_when_alert_and_rule_match(
)
await data_connection.execute(statement)
savepoint = data_connection.begin_nested()
with pytest.raises(IntegrityError):
async with data_connection.begin_nested():
async with savepoint:
await data_connection.execute(statement)
+245 -239
View File
@@ -1,239 +1,245 @@
import hashlib
import json
import pandas as pd
import pytest
from app.etl.historical_import import (
SOURCE_NAME,
build_reading_batch,
classify_quality,
compute_sha256,
load_metadata,
normalize_timestamps,
validate_source,
)
def make_metadata() -> dict:
return {
"total_records": 2,
"sites": {
"SITE001": {},
},
}
def make_dataframe() -> pd.DataFrame:
return pd.DataFrame(
[
{
"timestamp": "2023-01-01 00:00:00",
"site_id": "SITE001",
"site_type": "office",
"site_name": "Site 1",
"consumption_kwh": 10.5,
"consumption_euros": 2.5,
"temperature_celsius": 20.0,
"humidity_percent": 50.0,
"solar_irradiance_wm2": 0.0,
"hour": 0,
"day_of_week": 6,
"day_name": "Sunday",
"month": 1,
"is_weekend": True,
"is_working_hours": False,
},
{
"timestamp": "2023-01-01 01:00:00",
"site_id": "SITE001",
"site_type": "office",
"site_name": "Site 1",
"consumption_kwh": 11.0,
"consumption_euros": 2.7,
"temperature_celsius": 19.5,
"humidity_percent": 52.0,
"solar_irradiance_wm2": 0.0,
"hour": 1,
"day_of_week": 6,
"day_name": "Sunday",
"month": 1,
"is_weekend": True,
"is_working_hours": False,
},
]
)
def test_compute_sha256(tmp_path):
file_path = tmp_path / "dataset.csv"
content = b"hello-enervision"
file_path.write_bytes(content)
expected = hashlib.sha256(content).hexdigest()
assert compute_sha256(file_path) == expected
def test_load_metadata(tmp_path):
metadata_path = tmp_path / "metadata.json"
metadata = {
"total_records": 2,
"sites": {
"SITE001": {},
},
}
metadata_path.write_text(
json.dumps(metadata),
encoding="utf-8",
)
assert load_metadata(metadata_path) == metadata
def test_validate_source_accepts_valid_dataset():
frame = make_dataframe()
validate_source(
frame,
make_metadata(),
)
def test_validate_source_rejects_missing_column():
frame = make_dataframe().drop(columns=["consumption_kwh"])
with pytest.raises(
ValueError,
match="Colonnes obligatoires absentes",
):
validate_source(
frame,
make_metadata(),
)
def test_validate_source_rejects_duplicates():
frame = make_dataframe()
frame.loc[1, "timestamp"] = frame.loc[
0,
"timestamp",
]
with pytest.raises(
ValueError,
match="doublons",
):
validate_source(
frame,
make_metadata(),
)
def test_validate_source_rejects_unknown_site():
frame = make_dataframe()
frame.loc[1, "site_id"] = "SITE999"
with pytest.raises(
ValueError,
match="Sites incohérents",
):
validate_source(
frame,
make_metadata(),
)
def test_normalize_timestamps_adds_timezone():
frame = make_dataframe()
normalized = normalize_timestamps(
frame,
"UTC",
)
assert normalized["timestamp"].dt.tz is not None
assert "_source_timestamp" in normalized.columns
def test_classify_quality_good():
row = make_dataframe().iloc[0].to_dict()
quality, reasons = classify_quality(row)
assert quality == "good"
assert reasons == []
def test_classify_quality_degraded_when_consumption_missing():
row = make_dataframe().iloc[0].to_dict()
row["consumption_kwh"] = None
quality, reasons = classify_quality(row)
assert quality == "degraded"
assert "missing:consumption_kwh" in reasons
def test_build_reading_batch_respects_database_contract():
frame = normalize_timestamps(
make_dataframe(),
"UTC",
)
rows = build_reading_batch(
frame.iloc[:1],
dataset_id=3,
)
assert len(rows) == 1
row = rows[0]
assert row["dataset_id"] == 3
# Important :
# contrainte ck_reading_dataset_source.
assert row["source"] == "csv"
assert SOURCE_NAME == "csv"
# Important :
# contrainte ck_reading_imputation.
assert row["imputed_values"] is None
assert row["imputation_method"] is None
assert row["data_quality"] == "good"
assert row["null_reasons"] == []
def test_build_reading_batch_keeps_missing_values():
frame = make_dataframe()
frame.loc[0, "temperature_celsius"] = None
frame = normalize_timestamps(
frame,
"UTC",
)
rows = build_reading_batch(
frame.iloc[:1],
dataset_id=3,
)
row = rows[0]
assert row["temperature_celsius"] is None
assert "missing:temperature_celsius" in row["null_reasons"]
# RAW ingestion : aucune imputation.
assert row["imputed_values"] is None
assert row["imputation_method"] is None
import hashlib
import json
import pandas as pd
import pytest
from app.etl.historical_import import (
SOURCE_NAME,
build_reading_batch,
classify_quality,
compute_sha256,
load_metadata,
normalize_timestamps,
validate_source,
)
def make_metadata() -> dict:
return {
"total_records": 2,
"sites": {
"SITE001": {},
},
}
def make_dataframe() -> pd.DataFrame:
return pd.DataFrame(
[
{
"timestamp": "2023-01-01 00:00:00",
"site_id": "SITE001",
"site_type": "office",
"site_name": "Site 1",
"consumption_kwh": 10.5,
"consumption_euros": 2.5,
"temperature_celsius": 20.0,
"humidity_percent": 50.0,
"solar_irradiance_wm2": 0.0,
"hour": 0,
"day_of_week": 6,
"day_name": "Sunday",
"month": 1,
"is_weekend": True,
"is_working_hours": False,
},
{
"timestamp": "2023-01-01 01:00:00",
"site_id": "SITE001",
"site_type": "office",
"site_name": "Site 1",
"consumption_kwh": 11.0,
"consumption_euros": 2.7,
"temperature_celsius": 19.5,
"humidity_percent": 52.0,
"solar_irradiance_wm2": 0.0,
"hour": 1,
"day_of_week": 6,
"day_name": "Sunday",
"month": 1,
"is_weekend": True,
"is_working_hours": False,
},
]
)
def test_compute_sha256(tmp_path):
file_path = tmp_path / "dataset.csv"
content = b"hello-enervision"
file_path.write_bytes(content)
expected = hashlib.sha256(content).hexdigest()
assert compute_sha256(file_path) == expected
def test_load_metadata(tmp_path):
metadata_path = tmp_path / "metadata.json"
metadata = {
"total_records": 2,
"sites": {
"SITE001": {},
},
}
metadata_path.write_text(
json.dumps(metadata),
encoding="utf-8",
)
assert load_metadata(metadata_path) == metadata
def test_validate_source_accepts_valid_dataset():
frame = make_dataframe()
validate_source(
frame,
make_metadata(),
)
def test_validate_source_rejects_missing_column():
frame = make_dataframe().drop(columns=["consumption_kwh"])
metadata = make_metadata()
with pytest.raises(
ValueError,
match="Colonnes obligatoires absentes",
):
validate_source(
frame,
metadata,
)
def test_validate_source_rejects_duplicates():
frame = make_dataframe()
frame.loc[1, "timestamp"] = frame.loc[
0,
"timestamp",
]
metadata = make_metadata()
with pytest.raises(
ValueError,
match="doublons",
):
validate_source(
frame,
metadata,
)
def test_validate_source_rejects_unknown_site():
frame = make_dataframe()
frame.loc[1, "site_id"] = "SITE999"
metadata = make_metadata()
with pytest.raises(
ValueError,
match="Sites incohérents",
):
validate_source(
frame,
metadata,
)
def test_normalize_timestamps_adds_timezone():
frame = make_dataframe()
normalized = normalize_timestamps(
frame,
"UTC",
)
assert normalized["timestamp"].dt.tz is not None
assert "_source_timestamp" in normalized.columns
def test_classify_quality_good():
row = make_dataframe().iloc[0].to_dict()
quality, reasons = classify_quality(row)
assert quality == "good"
assert reasons == []
def test_classify_quality_degraded_when_consumption_missing():
row = make_dataframe().iloc[0].to_dict()
row["consumption_kwh"] = None
quality, reasons = classify_quality(row)
assert quality == "degraded"
assert "missing:consumption_kwh" in reasons
def test_build_reading_batch_respects_database_contract():
frame = normalize_timestamps(
make_dataframe(),
"UTC",
)
rows = build_reading_batch(
frame.iloc[:1],
dataset_id=3,
)
assert len(rows) == 1
row = rows[0]
assert row["dataset_id"] == 3
# Important :
# contrainte ck_reading_dataset_source.
assert row["source"] == "csv"
assert SOURCE_NAME == "csv"
# Important :
# contrainte ck_reading_imputation.
assert row["imputed_values"] is None
assert row["imputation_method"] is None
assert row["data_quality"] == "good"
assert row["null_reasons"] == []
def test_build_reading_batch_keeps_missing_values():
frame = make_dataframe()
frame.loc[0, "temperature_celsius"] = None
frame = normalize_timestamps(
frame,
"UTC",
)
rows = build_reading_batch(
frame.iloc[:1],
dataset_id=3,
)
row = rows[0]
assert row["temperature_celsius"] is None
assert "missing:temperature_celsius" in row["null_reasons"]
# RAW ingestion : aucune imputation.
assert row["imputed_values"] is None
assert row["imputation_method"] is None
@@ -49,8 +49,10 @@ async def test_the_database_refuses_to_mutate_the_audit_log(
) -> None:
await une_ligne(session)
requete = text(instruction)
with pytest.raises(DBAPIError, match="ajout seul"):
await session.execute(text(instruction))
await session.execute(requete)
await session.rollback()
@@ -131,11 +131,14 @@ async def test_the_database_refuses_two_tokens_sharing_a_fingerprint(
user_agent=None,
)
empreinte = fingerprint_refresh(secret)
expiration = datetime.now(UTC) + DUREE
with pytest.raises(IntegrityError):
await depot.create(
user_id=compte,
token_hash=fingerprint_refresh(secret),
expires_at=datetime.now(UTC) + DUREE,
token_hash=empreinte,
expires_at=expiration,
client_ip=None,
user_agent=None,
)
@@ -178,12 +178,16 @@ async def test_the_database_refuses_two_tokens_sharing_a_fingerprint(
user_agent=None,
)
famille = uuid.uuid4()
empreinte = fingerprint_refresh(secret)
expiration = datetime.now(UTC) + DUREE
with pytest.raises(IntegrityError):
await depot.create(
user_id=compte,
family_id=uuid.uuid4(),
token_hash=fingerprint_refresh(secret),
expires_at=datetime.now(UTC) + DUREE,
family_id=famille,
token_hash=empreinte,
expires_at=expiration,
client_ip=None,
user_agent=None,
)
+5 -7
View File
@@ -31,14 +31,12 @@ async def test_the_database_refuses_an_email_written_in_upper_case(
) -> None:
saisie = adresse().upper()
requete = text(
"insert into app_user (email, password_hash, role) values (:e, '$argon2id$x', 'lecteur')"
)
with pytest.raises(IntegrityError):
await session.execute(
text(
"insert into app_user (email, password_hash, role) "
"values (:e, '$argon2id$x', 'lecteur')"
),
{"e": saisie},
)
await session.execute(requete, {"e": saisie})
await session.rollback()
+4 -6
View File
@@ -116,13 +116,11 @@ async def test_list_history_normalizes_naive_datetimes_to_utc() -> None:
async def test_list_history_raises_when_start_is_after_end() -> None:
service = ReadingService(readings=FakeRepository([]))
debut = datetime(2026, 9, 2, tzinfo=UTC)
fin = datetime(2026, 9, 1, tzinfo=UTC)
with pytest.raises(FenetreInverseeError):
await service.list_history(
start=datetime(2026, 9, 2, tzinfo=UTC),
end=datetime(2026, 9, 1, tzinfo=UTC),
limit=500,
offset=0,
)
await service.list_history(start=debut, end=fin, limit=500, offset=0)
async def test_list_history_raises_when_start_equals_end() -> None:
+3 -1
View File
@@ -235,5 +235,7 @@ async def test_every_operation_refuses_an_unknown_account(action: str) -> None:
if action == "set_active":
arguments["is_active"] = False
methode = getattr(attirail.service, action)
with pytest.raises(UserNotFoundError):
await getattr(attirail.service, action)(**arguments)
await methode(**arguments)
+6 -2
View File
@@ -19,13 +19,17 @@ def test_build_parser_reads_the_create_admin_arguments() -> None:
def test_build_parser_requires_a_subcommand() -> None:
parser = cli.build_parser()
with pytest.raises(SystemExit):
cli.build_parser().parse_args([])
parser.parse_args([])
def test_build_parser_requires_an_email() -> None:
parser = cli.build_parser()
with pytest.raises(SystemExit):
cli.build_parser().parse_args(["create-admin"])
parser.parse_args(["create-admin"])
def test_read_password_generates_a_long_secret_when_asked(
+4 -1
View File
@@ -1,4 +1,4 @@
# Conventions de tests unitaires — Frontend
# Conventions de tests unitaires : Frontend
## Outil
Vitest (intégré nativement à Angular CLI, pas d'installation à faire).
@@ -83,3 +83,6 @@ describe('MonComposant', () => {
## Lancer les tests
- Développement (mode watch) : `npm test`
- Rapport de couverture (CI) : `npm run test:ci -- --coverage`, puis ouvrir `coverage/index.html`
- Un fichier ou un dossier seulement :
`npx ng test --watch=false --coverage=false --include=src/app/core/services/alerts.service.spec.ts`
(répéter `--include` pour plusieurs cibles ; un dossier joue tous ses specs)
@@ -2,53 +2,63 @@ import { Alert } from '../../shared/models/alert.model';
export const ALERTS_FIXTURE: Alert[] = [
{
alert_id: 'ALR-SITE002-1718458320',
timestamp: '2026-09-15T11:12:00',
alert_id: 5,
site_id: 'SITE002',
timestamp: '2026-09-15T11:12:00Z',
type: 'threshold',
severity: 'critical',
type: 'outage',
message: 'Risque de surcharge sur Usine Lyon Vénissieux',
message: 'Puissance appelée 812.5 kW au-dessus de la capacité du site (720.0 kW)',
value: 812.5,
threshold: 720.0,
metric: 'consumption_kw',
prediction_id: null,
},
{
alert_id: 'ALR-SITE003-1718458321',
timestamp: '2026-09-15T11:05:00',
alert_id: 4,
site_id: 'SITE003',
timestamp: '2026-09-15T11:05:00Z',
type: 'outage',
severity: 'critical',
type: 'sensor',
message: 'Perte réseau totale sur Data Center Marseille',
value: 0,
threshold: 0,
message: 'Aucune lecture depuis 5:00:00 (dernière lecture : 2026-09-15T06:05:00+00:00)',
value: null,
threshold: null,
metric: null,
prediction_id: null,
},
{
alert_id: 'ALR-SITE005-1718458322',
timestamp: '2026-09-15T10:47:00',
alert_id: 3,
site_id: 'SITE005',
timestamp: '2026-09-15T10:47:00Z',
type: 'spike',
severity: 'high',
type: 'threshold',
message: 'Usine Toulouse approche de son seuil de capacité',
message: 'Variation brutale entre deux lectures consécutives (260.0 kW -> 410.0 kW)',
value: 410.0,
threshold: 480.0,
threshold: 260.0,
metric: 'consumption_kw',
prediction_id: null,
},
{
alert_id: 'ALR-SITE006-1718458323',
timestamp: '2026-09-15T10:30:00',
alert_id: 2,
site_id: 'SITE006',
severity: 'medium',
timestamp: '2026-09-15T10:30:00Z',
type: 'sensor',
message: 'Capteur de température défaillant sur Bureau Lille',
value: 0,
threshold: 0,
severity: 'medium',
message: 'Qualité de mesure degraded (capteur hors ligne, valeur nulle)',
value: null,
threshold: null,
metric: null,
prediction_id: null,
},
{
alert_id: 'ALR-SITE004-1718458324',
timestamp: '2026-09-15T09:58:00',
alert_id: 1,
site_id: 'SITE004',
severity: 'low',
timestamp: '2026-09-15T09:58:00Z',
type: 'anomaly',
message: 'Comportement de consommation inhabituel sur Bureau Bordeaux',
severity: 'low',
message: 'Écart de 13% entre la consommation mesurée (62.0 kWh) et la prévision (55.0 kWh)',
value: 62.0,
threshold: 55.0,
metric: 'consumption_kwh',
prediction_id: 42,
},
];
@@ -3,6 +3,20 @@ import { provideHttpClient } from '@angular/common/http';
import { provideHttpClientTesting, HttpTestingController } from '@angular/common/http/testing';
import { AlertsService } from './alerts.service';
import { environment } from '../../../environments/environment';
import { Alert } from '../../shared/models/alert.model';
const ALERT_API: Alert = {
alert_id: 1,
site_id: 'site-1',
timestamp: '2026-09-16T00:00:00Z',
type: 'threshold',
severity: 'high',
message: 'Dépassement du seuil configuré',
value: 812.5,
threshold: 720.0,
metric: 'consumption_kw',
prediction_id: null,
};
describe('AlertsService', () => {
let service: AlertsService;
@@ -18,26 +32,35 @@ describe('AlertsService', () => {
afterEach(() => httpMock.verify());
it("appelle le bon endpoint et retourne un tableau d'alertes", () => {
let result: unknown;
it("appelle le bon endpoint sans paramètre et retourne un tableau d'alertes", () => {
let result: Alert[] = [];
service.getAlerts().subscribe((r) => (result = r));
const req = httpMock.expectOne(`${environment.apiUrl}/alerts`);
expect(req.request.method).toBe('GET');
const req = httpMock.expectOne(
(r) => r.url === `${environment.apiUrl}/alerts` && r.method === 'GET',
);
expect(req.request.params.keys()).toEqual([]);
req.flush([ALERT_API]);
req.flush([
{
alert_id: 'ALR-TEST-1',
timestamp: '2026-09-15T12:00:00',
site_id: 'SITE001',
severity: 'high',
type: 'threshold',
message: 'Test',
value: 100,
threshold: 90,
},
]);
expect(result.length).toBe(1);
expect(result[0].alert_id).toBe(1);
expect(result[0].prediction_id).toBeNull();
});
expect((result as unknown[]).length).toBe(1);
it('transmet les filtres site_id et severity en paramètres de requête', () => {
service.getAlerts({ site_id: 'SITE001', severity: 'high' }).subscribe();
const req = httpMock.expectOne((r) => r.url === `${environment.apiUrl}/alerts`);
expect(req.request.params.get('site_id')).toBe('SITE001');
expect(req.request.params.get('severity')).toBe('high');
req.flush([]);
});
it('ne pose pas de paramètre pour un filtre omis', () => {
service.getAlerts({ site_id: 'SITE001' }).subscribe();
const req = httpMock.expectOne((r) => r.url === `${environment.apiUrl}/alerts`);
expect(req.request.params.has('severity')).toBe(false);
req.flush([]);
});
});
@@ -1,13 +1,25 @@
import { Service, inject } from '@angular/core';
import { HttpClient } from '@angular/common/http';
import { HttpClient, HttpParams } from '@angular/common/http';
import { environment } from '../../../environments/environment';
import { Alert } from '../../shared/models/alert.model';
import { Alert, AlertSeverity } from '../../shared/models/alert.model';
export interface AlertFilters {
site_id?: string;
severity?: AlertSeverity;
}
@Service()
export class AlertsService {
private http = inject(HttpClient);
getAlerts() {
return this.http.get<Alert[]>(`${environment.apiUrl}/alerts`);
getAlerts(filters: AlertFilters = {}) {
let params = new HttpParams();
if (filters.site_id) {
params = params.set('site_id', filters.site_id);
}
if (filters.severity) {
params = params.set('severity', filters.severity);
}
return this.http.get<Alert[]>(`${environment.apiUrl}/alerts`, { params });
}
}
@@ -0,0 +1,37 @@
import {
LIBELLE_PAR_SEVERITE,
LIBELLE_PAR_TYPE,
SEVERITES,
TON_PAR_SEVERITE,
TYPES_ALERTE,
UNITE_PAR_METRIQUE,
} from './alert-presentation';
describe('alert-presentation', () => {
it('distingue le ton des sévérités high et critical', () => {
expect(TON_PAR_SEVERITE.high).toBe('danger');
expect(TON_PAR_SEVERITE.critical).toBe('critical');
expect(TON_PAR_SEVERITE.high).not.toBe(TON_PAR_SEVERITE.critical);
});
it("n'affiche pas une alerte faible avec le ton de succès", () => {
expect(TON_PAR_SEVERITE.low).toBe('neutral');
expect(TON_PAR_SEVERITE.medium).toBe('warning');
});
it('donne un libellé français à chaque sévérité et à chaque type', () => {
for (const severite of SEVERITES) {
expect(LIBELLE_PAR_SEVERITE[severite]).toBeTruthy();
}
for (const type of TYPES_ALERTE) {
expect(LIBELLE_PAR_TYPE[type]).toBeTruthy();
}
expect(SEVERITES.length).toBe(4);
expect(TYPES_ALERTE.length).toBe(5);
});
it('associe une unité à chaque métrique du contrat', () => {
expect(UNITE_PAR_METRIQUE.consumption_kw).toBe('kW');
expect(UNITE_PAR_METRIQUE.consumption_kwh).toBe('kWh');
});
});
@@ -0,0 +1,41 @@
import { BadgeTone } from '../components/ui/badge/badge';
import { AlertMetric, AlertSeverity, AlertType } from './alert.model';
// Pourquoi : `low` en neutre plutôt qu'en vert, une alerte faible reste une alerte ; le vert se
// lisait comme « tout va bien » à côté des rouges.
export const TON_PAR_SEVERITE: Record<AlertSeverity, BadgeTone> = {
low: 'neutral',
medium: 'warning',
high: 'danger',
critical: 'critical',
};
export const LIBELLE_PAR_SEVERITE: Record<AlertSeverity, string> = {
low: 'Faible',
medium: 'Moyenne',
high: 'Élevée',
critical: 'Critique',
};
export const LIBELLE_PAR_TYPE: Record<AlertType, string> = {
spike: 'Pic de consommation',
threshold: 'Seuil dépassé',
anomaly: 'Anomalie',
outage: 'Coupure',
sensor: 'Capteur',
};
export const UNITE_PAR_METRIQUE: Record<AlertMetric, string> = {
consumption_kw: 'kW',
consumption_kwh: 'kWh',
};
export const SEVERITES: readonly AlertSeverity[] = ['low', 'medium', 'high', 'critical'];
export const TYPES_ALERTE: readonly AlertType[] = [
'spike',
'threshold',
'anomaly',
'outage',
'sensor',
];
@@ -1,13 +1,16 @@
export type AlertSeverity = 'low' | 'medium' | 'high' | 'critical';
export type AlertType = 'spike' | 'threshold' | 'anomaly' | 'outage' | 'sensor';
export type AlertMetric = 'consumption_kw' | 'consumption_kwh';
export interface Alert {
alert_id: string;
timestamp: string;
alert_id: number;
site_id: string;
severity: AlertSeverity;
timestamp: string;
type: AlertType;
severity: AlertSeverity;
message: string;
value: number;
threshold: number;
value: number | null;
threshold: number | null;
metric: AlertMetric | null;
prediction_id: number | null;
}
+15
View File
@@ -29,3 +29,18 @@
color: var(--color-disabled);
margin-top: 0.25rem;
}
// Piège : le chevron est un SVG en data URI, où aucun token CSS n'est lisible ; sa couleur
// reprend en dur la valeur de --color-text-muted.
.form-select {
@extend .form-input;
padding-right: 2.25rem;
color: var(--color-text);
background-color: var(--color-surface);
background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20' fill='none' stroke='%236b7280' stroke-width='1.5' stroke-linecap='round' stroke-linejoin='round'%3E%3Cpath d='M6 8l4 4 4-4'/%3E%3C/svg%3E");
background-repeat: no-repeat;
background-position: right 0.6rem center;
background-size: 1rem;
appearance: none;
cursor: pointer;
}
+1
View File
@@ -3,6 +3,7 @@
{
"compileOnSave": false,
"compilerOptions": {
"strict": true,
"noImplicitOverride": true,
"noPropertyAccessFromIndexSignature": true,
"noImplicitReturns": true,
@@ -24,12 +24,13 @@ seule fois dans `src/styles.scss`. Disponibles partout sans import supplémentai
| `--shadow-card` | Ombre portée des cartes |
| `--space-1` à `--space-5` | Échelle d'espacement (0.35rem à 2.5rem) |
Les classes de formulaire partagées (`.form-label`, `.form-input`, `.form-hint`) sont dans
`apps/frontend/src/styles/_forms.scss`, importées globalement de la même façon. Elles
s'appliquent directement à des `<label>`/`<input>` natifs liés par `formControlName` : pas de
composant `ControlValueAccessor` dédié, le gain n'en vaut pas la complexité pour des formulaires
aussi simples que ceux de ce projet. Les erreurs de formulaire, elles, s'affichent via
`<ev-alert severity="danger">`, pas une classe dédiée.
Les classes de formulaire partagées (`.form-label`, `.form-input`, `.form-select`, `.form-hint`)
sont dans `apps/frontend/src/styles/_forms.scss`, importées globalement de la même façon. Elles
s'appliquent directement à des `<label>`/`<input>`/`<select>` natifs, liés par `formControlName` ou
par un simple `(change)` : pas de composant `ControlValueAccessor` dédié, le gain n'en vaut pas la
complexité pour des formulaires aussi simples que ceux de ce projet. `.form-select` habille un
`<select>` natif avec la bordure et le focus de `.form-input`, plus un chevron. Les erreurs de
formulaire, elles, s'affichent via `<ev-alert severity="danger">`, pas une classe dédiée.
La classe `.auth-page` (`apps/frontend/src/styles/_auth-page.scss`, importée globalement) porte
le fond dégradé et le centrage commun aux pages d'authentification (`login`, `change-password`,
+13 -4
View File
@@ -44,6 +44,7 @@ flowchart TB
subgraph sq["SonarQube · sonarqube.yml"]
sb1["build-front / test-front"]
sb2["build-back / test-back"]
sb3["test-ml"]
sscan["sonarqube<br/>quality gate SonarCloud"]
end
@@ -132,10 +133,18 @@ partie de la suite, et son taux n'aurait aucun sens face au seuil de 85 %.
## SonarCloud, et l'incident qui a immobilisé trois PR
Le workflow `sonarqube.yml` exécute quatre jobs de préparation (`build-front`, `test-front`,
`build-back`, `test-back`) qui produisent chacun un rapport de couverture en artefact, puis un
cinquième job qui les télécharge et lance `SonarSource/sonarqube-scan-action@v8` avec le secret
`SONAR_TOKEN`. Le périmètre est décrit par `sonar-project.properties` à la racine.
Le workflow `sonarqube.yml` exécute cinq jobs de préparation (`build-front`, `test-front`,
`build-back`, `test-back`, `test-ml`) dont les tests produisent chacun un rapport de couverture en
artefact, puis un dernier job qui les télécharge et lance `SonarSource/sonarqube-scan-action@v8`
avec le secret `SONAR_TOKEN`. Le périmètre est décrit par `sonar-project.properties` à la racine.
Le périmètre couvre `apps/frontend`, `apps/backend`, `ml/` et `etl/airflow` (les deux derniers
ajoutés après coup : ils n'étaient pas analysés, une PR qui ne touchait qu'eux ne lançait pas
Sonar). `ml/` publie `ml/coverage.xml` (`pytest-cov`, même mécanisme que le backend, sans seuil
propre : la gate porte sur le code neuf). `etl/airflow` est exclu de la **couverture**
(`sonar.coverage.exclusions`) : ses tests ne font que charger les DAGs, ils ne mesurent rien.
Piège : tout nouveau dossier de tests doit être déclaré dans `sonar.tests`, faute de quoi il est
compté comme code de production non couvert (cf. l'incident ci-dessous).
**L'incident, à raconter tel quel.** Les 18 et 19 septembre, trois PR (#103, #105, #107) sont
restées bloquées sur une quality gate rouge annonçant une couverture du code neuf à 0 %, alors que
+1 -1
View File
@@ -103,7 +103,7 @@ prevision (utile plus tard pour comparer prevision et realise, surveillance de d
uv run ruff check . # lint
uv run ruff format . # format
uv run mypy enervision_ml tests # typage strict
uv run pytest # tests
uv run pytest # tests + couverture (ml/coverage.xml avec --cov-report=xml, lu par Sonar)
```
Depuis la racine du monorepo, via le `Makefile` : `make install-ml`, `make ml-lint`,
+11 -1
View File
@@ -17,6 +17,7 @@ dev = [
"ruff>=0.16.7",
"mypy>=2.3.1",
"pytest>=9.1.1",
"pytest-cov>=7.1.0",
"pandas-stubs>=3.0.5.260914",
]
@@ -75,5 +76,14 @@ ignore_missing_imports = true
[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-q --strict-markers -m 'not integration'"
addopts = "-q --strict-markers -m 'not integration' --cov=enervision_ml --cov-report=term-missing"
markers = ["integration: requiert une base PostgreSQL joignable"]
# Rapport lu par SonarCloud (`ml/coverage.xml`, cf. sonar-project.properties), meme mecanisme que
# apps/backend. Pas de seuil ici : celui de la quality gate porte sur le code nouveau.
[tool.coverage.run]
source = ["enervision_ml"]
branch = true
[tool.coverage.report]
show_missing = true
Generated
+55
View File
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]
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name = "coverage"
version = "7.16.1"
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@@ -494,6 +533,7 @@ dev = [
{ name = "mypy" },
{ name = "pandas-stubs" },
{ name = "pytest" },
{ name = "pytest-cov" },
{ name = "ruff" },
]
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{ name = "mypy", specifier = ">=2.3.1" },
{ name = "pandas-stubs", specifier = ">=3.0.5.260914" },
{ name = "pytest", specifier = ">=9.1.1" },
{ name = "pytest-cov", specifier = ">=7.1.0" },
{ name = "ruff", specifier = ">=0.16.7" },
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+6 -4
View File
@@ -3,15 +3,17 @@ sonar.organization=groupe3-ener-vision
sonar.sourceEncoding=UTF-8
# Dossier contenant le code source
sonar.sources=apps/frontend/src,apps/backend
sonar.sources=apps/frontend/src,apps/backend,ml,etl/airflow
# Dossier contenant les tests
sonar.tests=apps/frontend/src,apps/backend/tests
sonar.tests=apps/frontend/src,apps/backend/tests,ml/tests,etl/airflow/tests
sonar.test.inclusions=**/*.spec.ts,**/*.test.ts,**/*test_*.py,**/*test.py
# Liste des fichiers et dossiers à exclure de l'analyse
sonar.exclusions=.pytest_cache,.venv,alembic,tests,**/*/node_modules/**,**/*/dist/**,**/*/build/**,**/*.spec.ts,**/*.test.ts,**/*test_*.py,**/*test.py,**/*.spec.ts
sonar.exclusions=.pytest_cache,.venv,.airflow_home,alembic,tests,ml/data/**,ml/models/**,ml/mlruns/**,ml/mlartifacts/**,**/*/node_modules/**,**/*/dist/**,**/*/build/**,**/*.spec.ts,**/*.test.ts,**/*test_*.py,**/*test.py,**/*.spec.ts
# Chemin vers le rapport de couverture de code
# Fichier généré par Pytest
sonar.python.coverage.reportPaths=apps/backend/coverage.xml
sonar.python.coverage.reportPaths=apps/backend/coverage.xml,ml/coverage.xml
# Les DAGs n'ont pas de couverture mesurable : leurs tests ne font que les charger (DagBag)
sonar.coverage.exclusions=etl/airflow/**
sonar.javascript.lcov.reportPaths=apps/frontend/coverage/frontend/lcov.info