Add ruff (#33)

* add ruff

* ruff all files

* ruff fix

* add ruff check to tests

* more options for ruff, no notebooks for now

* ruff

* ruff

* ruff

* ruffs

* ruff

* ruff

* we sometimes use {} without f

* some ruff

* space

* mistake

* fix imports

* options

* ignore tensorflow warnings

Author: ismael-mendoza

.github/workflows/tests.yml

@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Checkout github repo
@@ -26,12 +26,15 @@ jobs:
           cache: 'pip'
 
       - name: Install dependencies
-        run: | 
+        run: |
           python -m pip install -U pip
           python -m pip install .
           python -m pip install .[dev]
           python -m pip install git+https://github.com/GalSim-developers/JAX-GalSim.git
 
+      - name: Run Ruff
+        run: ruff check --output-format=github .
+
       - name: Run Tests
         run: |
           pytest --durations=0

bpd/diagnostics.py

@@ -16,15 +16,15 @@ def get_contour_plot(
     kde: bool | int | float = False,
 ) -> Figure:
     c = ChainConsumer()
-    for name, samples in zip(names, samples_list):
+    for name, samples in zip(names, samples_list, strict=False):
         df = pd.DataFrame.from_dict(samples)
         c.add_chain(Chain(samples=df, name=name, kde=kde))
     c.add_truth(Truth(location=truth))
     return c.plotter.plot(figsize=figsize)
 
 
 def get_gauss_pc_fig(
-    ax: Axes, samples: np.ndarray, truth: float, param_name: str = None
+    ax: Axes, samples: np.ndarray, truth: float, param_name: str | None = None
 ) -> None:
     """Get a marginal pc figure assuming Gaussian distribution of samples."""
     assert samples.ndim == 2  # (n_chains, n_samples)
@@ -49,7 +49,7 @@ def get_gauss_pc_fig(
 
 
 def get_pc_fig(
-    ax: Axes, samples: np.ndarray, truth: float, param_name: str = None
+    ax: Axes, samples: np.ndarray, truth: float, param_name: str | None = None
 ) -> None:
     """Get a marginal probability calibration figure using `hpdi` from `numpyro`."""
     assert samples.ndim == 2  # (n_chains, n_samples)

bpd/initialization.py

@@ -26,7 +26,7 @@ def init_with_ball(
     """Sample ball given offset of each parameter."""
     new = {}
     keys = random.split(rng_key, len(true_params.keys()))
-    rng_key_dict = {p: k for p, k in zip(true_params, keys)}
+    rng_key_dict = {p: k for p, k in zip(true_params, keys, strict=False)}
 
     for p, centr in true_params.items():
         offset = offset_dict[p]

bpd/io.py

@@ -11,7 +11,6 @@
 def save_dataset(
     ds: dict[str, Array], fpath: str | Path, overwrite: bool = False
 ) -> None:
-
     if Path(fpath).exists() and not overwrite:
         raise IOError("overwriting existing ds")
     assert Path(fpath).suffix == ".npz"
@@ -22,6 +21,7 @@ def save_dataset(
 def load_dataset(fpath: str) -> dict[str, Array]:
     assert Path(fpath).exists(), "file path does not exists"
     assert Path(fpath).suffix == ".npz"
+
     ds = {}
 
     npzfile = jnp.load(fpath)

bpd/likelihood.py

@@ -17,7 +17,7 @@ def shear_loglikelihood_unreduced(
     # the priors are callables for now on only ellipticities
     # the interim_prior should have been used when obtaining e_obs from the chain (i.e. for now same sigma)
     # normalizatoin in priors can be ignored for now as alpha is fixed.
-    N, K, _ = e_post.shape
+    _, K, _ = e_post.shape  # (N, K, 2)
 
     e_post_mag = jnp.sqrt(e_post[..., 0] ** 2 + e_post[..., 1] ** 2)
     denom = interim_prior(e_post_mag)  # (N, K), can ignore angle in prior as uniform

bpd/pipelines/image_ellips.py

@@ -3,16 +3,14 @@
 
 import blackjax
 import jax.numpy as jnp
-from jax import Array
+from jax import Array, random
 from jax import jit as jjit
-from jax import random
 from jax._src.prng import PRNGKeyArray
 from jax.scipy import stats
 
 from bpd.chains import inference_loop
 from bpd.draw import draw_gaussian, draw_gaussian_galsim
 from bpd.noise import add_noise
-from bpd.pipelines.toy_ellips import do_inference
 from bpd.prior import ellip_mag_prior, sample_ellip_prior
 
 
@@ -122,7 +120,6 @@ def do_inference(
     target_acceptance_rate: float = 0.80,
     n_samples: int = 100,
 ):
-
     key1, key2 = random.split(rng_key)
 
     _logdensity = partial(logtarget_fnc, data=data)
@@ -164,7 +161,6 @@ def pipeline_image_interim_samples(
     background: float = 1.0,
     fft_size: int = 256,
 ):
-
     k1, k2 = random.split(rng_key)
 
     init_position = initialization_fnc(k1, true_params=true_params, data=target_image)

bpd/pipelines/shear_inference.py

@@ -2,9 +2,8 @@
 from typing import Callable
 
 import blackjax
-from jax import Array
+from jax import Array, random
 from jax import jit as jjit
-from jax import random
 from jax._src.prng import PRNGKeyArray
 from jax.scipy import stats
 

bpd/pipelines/toy_ellips.py

@@ -4,9 +4,8 @@
 import blackjax
 import jax.numpy as jnp
 import jax.scipy as jsp
-from jax import Array
+from jax import Array, random, vmap
 from jax import jit as jjit
-from jax import random, vmap
 from jax._src.prng import PRNGKeyArray
 
 from bpd.chains import inference_loop
@@ -73,7 +72,6 @@ def pipeline_toy_ellips_samples(
     k: int,
     n_warmup_steps: int = 500,
 ):
-
     k1, k2 = random.split(key)
 
     true_g = jnp.array([g1, g2])

pyproject.toml

@@ -2,42 +2,89 @@
 requires = ["setuptools>=45"]
 build-backend = "setuptools.build_meta"
 
+
 [project]
 name = "BPD"
 authors = [{ name = "Ismael Mendoza" }]
 description = "Bayesian Pixel Domain method for shear inference."
 version = "0.0.1"
 license = { file = "LICENSE" }
 readme = "README.md"
-dependencies = [
-    "numpy >=1.18.0",
-    "galsim >=2.3.0",
-    "jax >=0.4.30",
-    "jaxlib",
-    "blackjax >=1.2.0",
-    "numpyro >=0.15.0",
-]
+dependencies = ["numpy >=1.18.0", "galsim >=2.3.0", "jax >=0.4.30", "jaxlib", "blackjax >=1.2.0"]
+
 
 [project.optional-dependencies]
-dev = ["pytest", "click"]
+dev = ["pytest", "click", "ruff", "ChainConsumer"]
+
 
 [project.urls]
 home = "https://github.com/LSSTDESC/BPD"
 
+
 [tool.setuptools.packages.find]
-include = ["bpd*", "scripts*"]
+include = ["bpd*"]
+
+[tool.ruff]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".git-rewrite",
+    ".hg",
+    ".ipynb_checkpoints",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pyenv",
+    ".pytest_cache",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    ".vscode",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "site-packages",
+    "venv",
+]
+
+# Same as Black.
+line-length = 88
+indent-width = 4
+
+# Assume Python 3.8
+target-version = "py310"
+
+[tool.ruff.format]
+# Like Black, use double quotes for strings.
+quote-style = "double"
+
+# Like Black, indent with spaces, rather than tabs.
+indent-style = "space"
+
+# Like Black, respect magic trailing commas.
+skip-magic-trailing-comma = false
+
+# Like Black, automatically detect the appropriate line ending.
+line-ending = "auto"
+
+exclude = ["*.ipynb"]
 
 
-[tool.flake8]
-max-line-length = 88
-ignore = ["C901", "E203", "W503"]
-per-file-ignores = ["__init__.py:F401"]
+[tool.ruff.lint]
+select = ["E", "F", "I", "W", "B", "SIM", "PLE", "PLC", "PLW", "RUF"]
+ignore = ["C901", "E203", "E501", "E731", "PLC0206", "RUF027"]
+preview = true
+exclude = ["*.ipynb", "scripts/image_shear_gpu_one_galaxy.py", "scripts/benchmarks/*.py"]
 
 
-[tool.isort]
-profile = "black"
-multi_line_output = 3
-include_trailing_comma = true
-use_parentheses = true
-ensure_newline_before_comments = true
-line_length = 88
+[tool.pytest.ini_options]
+minversion = "6.0"
+addopts = "-rav"
+filterwarnings = ["ignore::DeprecationWarning:tensorflow.*"]

scripts/benchmarks/benchmark1.py

@@ -82,7 +82,7 @@
 def sample_ball(rng_key, center_params: dict):
     new = {}
     keys = random.split(rng_key, len(center_params.keys()))
-    rng_key_dict = {p: k for p, k in zip(center_params, keys)}
+    rng_key_dict = {p: k for p, k in zip(center_params, keys, strict=False)}
     for p in center_params:
         centr = center_params[p]
         if p == "f":
@@ -128,7 +128,6 @@ def draw_gal(f, hlr, g1, g2, x, y):
 
 
 def _logprob_fn(params, data):
-
     # prior
     prior = jnp.array(0.0, device=GPU)
     for p in ("f", "hlr", "g1", "g2"):  # uniform priors
@@ -148,7 +147,7 @@ def _logprob_fn(params, data):
 
 
 def _log_setup(snr: float):
-    with open(LOG_FILE, "a") as f:
+    with open(LOG_FILE, "a", encoding="utf-8") as f:
         print(file=f)
         print(
             f"""Running benchmark 1 with configuration as follows. Variable number of chains.
@@ -186,7 +185,6 @@ def _log_setup(snr: float):
 
 # vmap only rng_key
 def do_warmup(rng_key, init_position: dict, data):
-
     _logdensity = partial(_logprob_fn, data=data)
 
     warmup = blackjax.window_adaptation(
@@ -258,7 +256,6 @@ def main():
         _init_positions = {p: q[:n_chains] for p, q in all_init_positions.items()}
 
         if ii == 0:
-
             # compilation times
             t1 = time.time()
             (_sts, _tp), _ = jax.block_until_ready(
@@ -310,7 +307,7 @@ def main():
     jnp.save(filepath, results)
 
     _log_setup(snr)
-    with open(LOG_FILE, "a") as f:
+    with open(LOG_FILE, "a", encoding="utf-8") as f:
         print(file=f)
         print(f"results were saved to {filepath}", file=f)