style: Improve code aesthetics

clean: Unused variables

style: unused variable

tests/bias/test_qdm_bias_correction.py

@@ -363,7 +363,7 @@ def test_bc_trend_same_hist(tmp_path, fp_fut_cc, dist_params):
  assert np.allclose(corrected[idx], original[idx])
 
 
-def test_fwp_integration(tmp_path, fp_fut_cc):
+def test_fwp_integration(tmp_path):
  """Integration of the bias correction method into the forward pass
 
  Validate two aspects:
@@ -410,25 +410,17 @@ def test_fwp_integration(tmp_path, fp_fut_cc):
  out_dir = os.path.join(tmp_path, 'st_gan')
  model.save(out_dir)
 
- scalar = np.random.uniform(0.5, 1, (8, 8, 1))
- adder = np.random.uniform(0, 1, (8, 8, 1))
-
- with h5py.File(bias_fp, 'w') as f:
- f.create_dataset('U_100m_scalar', data=scalar)
- f.create_dataset('U_100m_adder', data=adder)
- f.create_dataset('V_100m_scalar', data=scalar)
- f.create_dataset('V_100m_adder', data=adder)
- f.create_dataset('latitude', data=lat_lon[..., 0])
- f.create_dataset('longitude', data=lat_lon[..., 1])
+ with h5py.File(bias_fp, "w") as f:
+ f.create_dataset("latitude", data=lat_lon[..., 0])
+ f.create_dataset("longitude", data=lat_lon[..., 1])
 
  s = lat_lon.shape[:2]
- for k,v in params.items():
- f.create_dataset(k, data=np.broadcast_to(v, (*s, v.size )))
+ for k, v in params.items():
+ f.create_dataset(k, data=np.broadcast_to(v, (*s, v.size)))
  f.attrs["dist"] = "empirical"
  f.attrs["sampling"] = "linear"
  f.attrs["log_base"] = 10
 
-
  bias_correct_kwargs = {'U_100m': {'feature_name': 'U_100m',
  'base_dset': 'Uref_100m',
  'bias_fp': bias_fp},
@@ -466,8 +458,12 @@ def test_fwp_integration(tmp_path, fp_fut_cc):
  bc_fwp = ForwardPass(bc_strat, chunk_index=ichunk)
 
  delta = bc_fwp.input_data - fwp.input_data
- assert np.allclose(delta[..., 0], -2.72, atol=1e-03), "U reference offset is -1"
- assert np.allclose(delta[..., 1], 2.72, atol=1e-03), "V reference offset is 1"
+ assert np.allclose(
+ delta[..., 0], -2.72, atol=1e-03
+ ), "U reference offset is -1"
+ assert np.allclose(
+ delta[..., 1], 2.72, atol=1e-03
+ ), "V reference offset is 1"
 
  delta = bc_fwp.run_chunk() - fwp.run_chunk()
  assert delta[..., 0].mean() < 0, "Predicted U should trend <0"