Add isort Workflow

.github/workflows/black.yml

@@ -8,6 +8,5 @@ jobs:
     steps:
     - uses: actions/checkout@v2
     - uses: actions/setup-python@v2
-    - uses: psf/black@stable
-      with:
-        version: "22.1.0"
+    - name: Check code with black
+      run: touch .env && docker-compose run black

.github/workflows/isort.yml

@@ -0,0 +1,12 @@
+name: Isort
+
+on: [push, pull_request]
+
+jobs:
+  isort:
+    runs-on: ubuntu-18.04
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+    - name: Check code with isort
+      run: touch .env && docker-compose run isort

Dockerfile.lint

@@ -0,0 +1,5 @@
+FROM python:3.9
+
+COPY . /src/nnfabrik
+
+RUN python -m pip install --requirement /src/nnfabrik/requirements-dev.txt

README.md

@@ -1,6 +1,7 @@
 # nnfabrik: a generalized model fitting pipeline
 
 ![Black](https://github.com/sinzlab/nnfabrik/workflows/Black/badge.svg)
+![ISort](https://github.com/sinzlab/nnfabrik/workflows/ISort/badge.svg)
 ![GitHub Pages](https://github.com/sinzlab/nnfabrik/workflows/GitHub%20Pages/badge.svg?branch=master)
 
 nnfabrik is a model fitting pipeline, mainly developed for neural networks, where training results (i.e. scores, and trained models) as well as any data related to models, trainers, and datasets used for training are stored in datajoint tables.
@@ -70,3 +71,31 @@ The documentation can be found [here](https://sinzlab.github.io/nnfabrik/). Plea
 ## :bug: Report bugs (or request features)
 
 In case you find a bug or would like to see some new features added to nnfabrik, please create an issue or contact any of the contributors.
+
+## :fire: How to contribute
+
+Pull requests (and issues) are always welcome. This section describes some
+preconditions that pull requests need to fulfill.
+
+### black
+
+This project uses the [black](https://github.com/psf/black) code formatter. You
+can check whether your changes comply with its style by running the following
+command:
+
+```bash
+docker-compose run black
+```
+
+Furthermore you can pass a path to the service to have black fix any errors in
+the Python modules it finds in the given path.
+
+### isort
+
+[isort](https://github.com/PyCQA/isort) is used to sort Python imports. You can check the order of imports by running the following command:
+
+```bash
+docker-compose run isort
+```
+
+The imports can be sorted by passing a path to the service.

docker-compose.yml

@@ -18,4 +18,22 @@ services:
     build: .
     volumes:
       - ".:/src/nnfabrik"
-    entrypoint: /bin/bash -c "cd /src/nnfabrik/docs/; while :; do make html; sleep 30; done"
+    entrypoint: /bin/bash -c "cd /src/nnfabrik/docs/; while :; do make html; sleep 30; done"
+
+  black:
+    build:
+      context: .
+      dockerfile: Dockerfile.lint
+    volumes:
+      - .:/src/nnfabrik
+    entrypoint: ["black"]
+    command: ["--check", "--diff", "/src/nnfabrik"]
+
+  isort:
+    build:
+      context: .
+      dockerfile: Dockerfile.lint
+    volumes:
+      - .:/src/nnfabrik
+    entrypoint: ["isort"]
+    command: ["--check-only", "--diff", "/src/nnfabrik"]

docs/conf.py

@@ -12,6 +12,7 @@
 #
 import os
 import sys
+
 import sphinx_rtd_theme
 
 sys.path.insert(0, os.path.abspath("../"))

nnfabrik/builder.py

@@ -1,8 +1,8 @@
-from . import utility
 from functools import partial
 
-from .utility.nnf_helper import split_module_name, dynamic_import
+from . import utility
 from .utility.nn_helpers import load_state_dict
+from .utility.nnf_helper import dynamic_import, split_module_name
 
 
 def resolve_fn(fn_name, default_base):

nnfabrik/examples/mnist/model.py

@@ -1,4 +1,5 @@
 from typing import Dict
+
 import numpy as np
 import torch
 import torch.nn as nn

nnfabrik/examples/mnist/trainer.py

@@ -1,9 +1,8 @@
-from typing import Dict, Tuple, Callable, List, Any
+from typing import Any, Callable, Dict, List, Tuple
 
-from tqdm import tqdm
 import torch
-from torch import nn
-from torch import optim
+from torch import nn, optim
+from tqdm import tqdm
 
 
 class MNISTTrainer:

nnfabrik/examples/mnist_checkpoint/trainer.py

@@ -1,8 +1,8 @@
-from typing import Dict, Tuple, Callable, Optional, List, Any
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
-from tqdm import tqdm
 import torch
 import torch.nn as nn
+from tqdm import tqdm
 
 from nnfabrik.examples.mnist.trainer import MNISTTrainer
 

nnfabrik/examples/mnist_transfer/trainer.py

@@ -3,14 +3,14 @@
 with our TransferredTrainedModel-table
 """
 
-from typing import Dict, Tuple, Callable
+from typing import Callable, Dict, Tuple
 
 import numpy as np
-from torch.utils.data import DataLoader
-from tqdm import tqdm
 import torch
 import torch.nn as nn
 import torch.optim as optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
 
 from nnfabrik.examples.mnist.trainer import MNISTTrainer
 

nnfabrik/examples/nnfabrik.py

@@ -1,14 +1,13 @@
 # importing the tables here is a trick to get IntelliSense to work
-from nnfabrik.main import Fabrikant, Trainer, Dataset, Model, Seed, my_nnfabrik
+from nnfabrik.main import Dataset, Fabrikant, Model, Seed, Trainer, my_nnfabrik
 from nnfabrik.templates.transfer.recipes import (
-    TrainedModelTransferRecipe,
     DatasetTransferRecipe,
     ModelTransferRecipe,
-    TrainerTransferRecipe,
+    TrainedModelTransferRecipe,
     TrainerDatasetTransferRecipe,
+    TrainerTransferRecipe,
 )
 
-
 # define nnfabrik tables here
 my_nnfabrik(
     "nnfabrik_example",

nnfabrik/main.py

@@ -1,18 +1,18 @@
-import warnings
 import types
-from typing import Union, Optional, MutableMapping, Tuple
+import warnings
+from typing import MutableMapping, Optional, Tuple, Union
 
 import datajoint as dj
 
 from .builder import (
-    resolve_model,
-    resolve_data,
-    resolve_trainer,
     get_data,
     get_model,
     get_trainer,
+    resolve_data,
+    resolve_model,
+    resolve_trainer,
 )
-from .utility.dj_helpers import make_hash, CustomSchema, Schema
+from .utility.dj_helpers import CustomSchema, Schema, make_hash
 from .utility.nnf_helper import cleanup_numpy_scalar
 
 if "nnfabrik.schema_name" in dj.config:

nnfabrik/templates/__init__.py

@@ -1,3 +1,3 @@
-from .trained_model import TrainedModelBase, DataInfoBase
 from .scoring import ScoringBase
+from .trained_model import DataInfoBase, TrainedModelBase
 from .transfer.transferred_trained_model import TransferredTrainedModelBase

nnfabrik/templates/checkpoint.py

@@ -6,7 +6,7 @@
 import torch
 
 from nnfabrik.templates.trained_model import TrainedModelBase
-from nnfabrik.utility.dj_helpers import make_hash, clone_conn, CustomSchema
+from nnfabrik.utility.dj_helpers import CustomSchema, clone_conn, make_hash
 
 
 def my_checkpoint(nnfabrik):

nnfabrik/templates/scoring.py

@@ -1,6 +1,8 @@
 import datajoint as dj
 import numpy as np
-from nnfabrik.main import Model, Dataset, Trainer, Seed, Fabrikant
+
+from nnfabrik.main import Dataset, Fabrikant, Model, Seed, Trainer
+
 from .trained_model import TrainedModelBase
 
 

nnfabrik/templates/trained_model.py

@@ -1,9 +1,11 @@
-import tempfile
-import torch
 import os
+import tempfile
 import warnings
+
 import datajoint as dj
+import torch
 from datajoint.fetch import DataJointError
+
 from ..builder import get_all_parts, get_model, get_trainer, resolve_fn
 from ..utility.dj_helpers import make_hash
 from .utility import find_object

nnfabrik/templates/transfer/transferred_trained_model.py

@@ -1,13 +1,14 @@
+import os
 import tempfile
 from collections import Mapping, Sequence
 
-import torch
-import os
 import datajoint as dj
 import numpy as np
+import torch
+
 from nnfabrik.templates.checkpoint import TrainedModelChkptBase
-from nnfabrik.utility.dj_helpers import gitlog, make_hash
 from nnfabrik.templates.trained_model import TrainedModelBase
+from nnfabrik.utility.dj_helpers import gitlog, make_hash
 
 
 class TransferredTrainedModelBase(TrainedModelBase):

nnfabrik/templates/utility.py

@@ -1,7 +1,8 @@
-from .. import main
 from types import ModuleType
 from typing import Union
 
+from .. import main
+
 
 def find_object(context: Union[ModuleType, dict], attribute: str, prop_name: str = None):
     """

nnfabrik/utility/__init__.py

@@ -1,3 +1 @@
-from . import dj_helpers
-from . import nnf_helper
-from . import nn_helpers
+from . import dj_helpers, nn_helpers, nnf_helper

nnfabrik/utility/dj_helpers.py

@@ -1,15 +1,15 @@
 # helper functions for use with DataJoint tables
 
-import warnings
-from datetime import datetime
 import hashlib
-import datajoint as dj
-from git import Repo, cmd
-import numpy as np
 import inspect
+import warnings
+from collections import Iterable, Mapping, OrderedDict
 from datetime import date, datetime
+
+import datajoint as dj
+import numpy as np
 from datajoint.utils import to_camel_case
-from collections import OrderedDict, Iterable, Mapping
+from git import Repo, cmd
 
 # try/except is necessary to support all versions of dj
 try:

nnfabrik/utility/hypersearch.py

@@ -1,8 +1,10 @@
+import datajoint as dj
 import numpy as np
 from ax.service.managed_loop import optimize
-from .nnf_helper import split_module_name, dynamic_import
+
 from nnfabrik.main import *
-import datajoint as dj
+
+from .nnf_helper import dynamic_import, split_module_name
 
 
 class Bayesian:

nnfabrik/utility/nn_helpers.py

@@ -1,10 +1,10 @@
 # helper functions concerning the ANN architecture
 
-import torch
-from torch import nn
+import random
 
 import numpy as np
-import random
+import torch
+from torch import nn
 
 
 def get_io_dims(data_loader):

nnfabrik/utility/nnf_helper.py

@@ -1,6 +1,8 @@
 from importlib import import_module
+
 import numpy as np
-from ..utility.dj_helpers import make_hash, cleanup_numpy_scalar
+
+from ..utility.dj_helpers import cleanup_numpy_scalar, make_hash
 
 
 def split_module_name(abs_class_name):

pyproject.toml

@@ -1,2 +1,5 @@
 [tool.black]
 line-length = 120
+
+[tool.isort]
+profile = "black"

requirements-dev.txt

@@ -0,0 +1,2 @@
+black==22.1.0
+isort==5.10.1

setup.py

@@ -1,7 +1,8 @@
 #!/usr/bin/env python
-from setuptools import setup, find_packages
 from os import path
 
+from setuptools import find_packages, setup
+
 here = path.abspath(path.dirname(__file__))
 
 setup(