lint tests

examples/monkeypatch.py

@@ -5,8 +5,6 @@
 convolution for later analysis.
 """
 
-from typing import Optional
-
 import torch
 
 from nequip.utils import Config, find_first_of_type

tests/unit/data/test_dataloader.py

@@ -9,12 +9,10 @@
 
 class TestInit:
     def test_init(self, npz_dataset):
-        dl = DataLoader(
-            npz_dataset, batch_size=2, shuffle=True, exclude_keys=["energy"]
-        )
+        DataLoader(npz_dataset, batch_size=2, shuffle=True, exclude_keys=["energy"])
 
     def test_subset(self, npz_dataset):
-        subset = npz_dataset[[1, 3]]
+        npz_dataset[[1, 3]]
 
 
 class TestLoop:

tests/unit/data/test_dataset.py

@@ -1,4 +1,3 @@
-from attr import fields
 import numpy as np
 import pytest
 import tempfile
@@ -8,7 +7,6 @@
 
 from ase.data import chemical_symbols
 from ase.io import write
-from torch.functional import _return_counts
 
 from nequip.data import (
     AtomicDataDict,

tests/unit/model/test_eng_force.py

@@ -3,7 +3,6 @@
 import logging
 import tempfile
 import torch
-from os.path import isfile
 
 import numpy as np
 

tests/unit/nn/test_embed.py

@@ -1,8 +1,5 @@
-import torch
-
 from e3nn.util.test import assert_auto_jitable
 
-from nequip.data import AtomicDataDict
 from nequip.utils.test import assert_AtomicData_equivariant
 from nequip.nn.radial_basis import BesselBasis
 from nequip.nn.cutoffs import PolynomialCutoff

tests/unit/trainer/test_metrics.py

@@ -1,10 +1,8 @@
-import inspect
 import pytest
 import torch
 from nequip.data import AtomicDataDict
 from nequip.train import Metrics
 
-from .test_loss import data
 
 # all the config to test init
 # only the last one will be used to test the loss and mae
@@ -96,7 +94,7 @@ def test_per_species(self, data, per_comp):
 @pytest.fixture(scope="class", params=metrics_tests)
 def metrics(request):
     """"""
-    coeffs = request.param
+    coeffs = request.param  # noqa
     instance = Metrics(components=request.param)
     yield instance
 

tests/unit/trainer/test_trainer.py

@@ -77,10 +77,10 @@ def test_duplicate_id_2(self, temp_data):
         minimal_config["root"] = temp_data
 
         model = DummyNet(3)
-        c1 = Trainer(model=model, **minimal_config)
+        Trainer(model=model, **minimal_config)
 
         with pytest.raises(RuntimeError):
-            c2 = Trainer(model=model, **minimal_config)
+            Trainer(model=model, **minimal_config)
 
 
 class TestSaveLoad:
@@ -203,10 +203,9 @@ def test_load_w_revision(self, trainer):
             assert trainer1.max_epochs == minimal_config["max_epochs"] * 2
 
     def test_restart_training(self, trainer, nequip_dataset):
-
-        model = trainer.model
-        device = trainer.device
-        optimizer = trainer.optim
+        _ = trainer.model
+        _ = trainer.device
+        _ = trainer.optim
         trainer.set_dataset(nequip_dataset)
         trainer.train()
 

tests/unit/utils/test_config.py

@@ -1,13 +1,8 @@
 """
 Config tests
 """
-
-
-import numpy as np
 import pytest
 
-import torch
-
 from os import remove
 
 from nequip.utils import Config

tests/unit/utils/test_instantiate.py

@@ -155,8 +155,8 @@ def __init__(self, cls_b, cls_b_kwargs):
 
 
 class C:
-    def __init__(self, cls_c, cls_c_kwargs):
-        self.c_obj = c_cls(**c_cls_kwargs)
+    def __init__(self, cls_c, cls_c_kwargs):  # noqa
+        self.c_obj = c_cls(**c_cls_kwargs)  # noqa
 
 
 def test_deep_nests():

tests/unit/utils/test_output.py

@@ -1,14 +1,9 @@
 """
 Config tests
 """
-
-
-import numpy as np
 import pytest
 import tempfile
-import torch
 
-from os import remove
 from os.path import isdir
 
 from nequip.utils.output import Output