🚨 Fix linting errors in smoke tests

data_safe_haven/infrastructure/components/dynamic/file_upload.py

@@ -29,7 +29,9 @@ def __init__(
         self.file_hash = file_hash
         self.file_target = file_target
         self.file_permissions = file_permissions
-        self.force_refresh = Output.from_input(force_refresh).apply(lambda force: force if force else False)
+        self.force_refresh = Output.from_input(force_refresh).apply(
+            lambda force: force if force else False
+        )
         self.subscription_name = subscription_name
         self.vm_name = vm_name
         self.vm_resource_group_name = vm_resource_group_name

data_safe_haven/infrastructure/stacks/sre/workspaces.py

@@ -177,10 +177,12 @@ def __init__(
         ]
 
         # Upload smoke tests
-        mustache_values={
+        mustache_values = {
             "check_uninstallable_packages": "0",
         }
-        file_uploads = [(FileReader(resources_path / "workspace" / "run_all_tests.bats"), "0444")]
+        file_uploads = [
+            (FileReader(resources_path / "workspace" / "run_all_tests.bats"), "0444")
+        ]
         for test_file in pathlib.Path(resources_path / "workspace").glob("test*"):
             file_uploads.append((FileReader(test_file), "0444"))
         for vm, vm_output in zip(vms, vm_outputs, strict=True):
@@ -189,7 +191,9 @@ def __init__(
                 file_smoke_test = FileUpload(
                     replace_separators(f"{self._name}_file_{file_upload.name}", "_"),
                     FileUploadProps(
-                        file_contents=file_upload.file_contents(mustache_values=mustache_values),
+                        file_contents=file_upload.file_contents(
+                            mustache_values=mustache_values
+                        ),
                         file_hash=file_upload.sha256(),
                         file_permissions=file_permissions,
                         file_target=f"/opt/tests/{file_upload.name}",

data_safe_haven/resources/workspace/test_databases_python.py

@@ -6,8 +6,17 @@
 import pymssql
 
 
-def test_database(server_name, hostname, port, db_type, db_name, username, password):
-    print(f"Attempting to connect to '{db_name}' on '{server_name}' via port {port}")
+def test_database(
+    server_name: str,
+    hostname: str,
+    port: int,
+    db_type: str,
+    db_name: str,
+    username: str,
+    password: str,
+) -> None:
+    msg = f"Attempting to connect to '{db_name}' on '{server_name}' via port {port}"
+    print(msg)  # noqa: T201
     username_full = f"{username}@{hostname}"
     cnxn = None
     if db_type == "mssql":
@@ -18,13 +27,15 @@ def test_database(server_name, hostname, port, db_type, db_name, username, passw
         connection_string = f"host={server_name} port={port} dbname={db_name} user={username_full} password={password}"
         cnxn = psycopg.connect(connection_string)
     else:
-        raise ValueError(f"Database type '{db_type}' was not recognised")
+        msg = f"Database type '{db_type}' was not recognised"
+        raise ValueError(msg)
     df = pd.read_sql("SELECT * FROM information_schema.tables;", cnxn)
     if df.size:
-        print(df.head(5))
-        print("All database tests passed")
+        print(df.head(5))  # noqa: T201
+        print("All database tests passed")  # noqa: T201
     else:
-        raise ValueError(f"Reading from database '{db_name}' failed.")
+        msg = f"Reading from database '{db_name}' failed."
+        raise ValueError(msg)
 
 
 # Parse command line arguments

data_safe_haven/resources/workspace/test_functionality_python.py

@@ -4,21 +4,21 @@
 from sklearn.linear_model import LogisticRegression
 
 
-def gen_data(n_samples, n_points):
+def gen_data(n_samples: int, n_points: int) -> pd.DataFrame:
     """Generate data for fitting"""
     target = np.random.binomial(n=1, p=0.5, size=(n_samples, 1))
     theta = np.random.normal(loc=0.0, scale=1.0, size=(1, n_points))
     means = np.mean(np.multiply(target, theta), axis=0)
     values = np.random.multivariate_normal(
         means, np.diag([1] * n_points), size=n_samples
     ).T
-    data = dict(("x{}".format(n), values[n]) for n in range(n_points))
+    data = {f"x{n}": values[n] for n in range(n_points)}
     data["y"] = target.reshape((n_samples,))
     data["weights"] = np.random.gamma(shape=1, scale=1.0, size=n_samples)
     return pd.DataFrame(data=data)
 
 
-def main():
+def main() -> None:
     """Logistic regression"""
     data = gen_data(100, 3)
     input_data = data.iloc[:, :-2]
@@ -29,8 +29,8 @@ def main():
     logit.fit(input_data, output_data, sample_weight=weights)
     logit.score(input_data, output_data, sample_weight=weights)
 
-    print("Logistic model ran OK")
-    print("All functionality tests passed")
+    print("Logistic model ran OK")  # noqa: T201
+    print("All functionality tests passed")  # noqa: T201
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -152,11 +152,15 @@ module = [
     "cryptography.*",
     "dns.*",
     "msal.*",
+    "numpy.*",
+    "pandas.*",
     "psycopg.*",
     "pulumi.*",
     "pulumi_azure_native.*",
+    "pymssql.*",
     "rich.*",
     "simple_acme_dns.*",
+    "sklearn.*",
     "typer.*",
     "websocket.*",
 ]