lint: ruff

.github/workflows/test.yml

@@ -26,7 +26,7 @@ jobs:
       - name: Linter
         run: |
           black --check src tests
-          ruff .
+          ruff check .
       - name: Annotate locations with typos
         uses: codespell-project/codespell-problem-matcher@v1
       - name: Codespell

examples/example_anat_EPI.py

@@ -41,7 +41,8 @@
 #
 # The simulation acquires
 # the data describe in a phantom. A phantom consists of fuzzy segmentation of
-# head tissue, and their MR intrisic parameters (density, T1, T2, T2*,  magnetic susceptibilities)
+# head tissue, and their MR intrisic parameters
+# (density, T1, T2, T2*,  magnetic susceptibilities)
 #
 # Here we use Brainweb reference mask and values for convenience.
 

examples/example_generate_phantom.py

@@ -5,17 +5,17 @@
 Example for generating a phantom and visualizing the contrast at different TE values.
 """
 
-import numpy as np
 from snake.core.phantom import Phantom
 from snake.core.engine.utils import get_ideal_phantom
 from snake.core.simulation import SimConfig, GreConfig
 
 
-# +
-# This notebook has interactive widgets, run it in google colab or locally to enjoy it fully
-# -
-
+# %%
+# This notebook has interactive widgets, run it in google colab or locally to
+# enjoy it fully
+#
 
+# %%
 shape = (181, 217, 181)
 TR = 100
 TE = 25

examples/example_gpu_anat_spirals.py

@@ -13,7 +13,6 @@
 # %%
 
 # Imports
-import numpy as np
 from snake.core.simulation import SimConfig, default_hardware, GreConfig
 from snake.core.phantom import Phantom
 from snake.core.smaps import get_smaps

src/snake/__init__.py

@@ -1 +1 @@
-"""SNAKE Package"""
+"""SNAKE Package."""

src/snake/core/engine/base.py

@@ -9,7 +9,6 @@
 from collections.abc import Mapping, Sequence
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
 from multiprocessing.managers import SharedMemoryManager
-from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Any, ClassVar
 
@@ -164,7 +163,6 @@ def __call__(
         **kwargs: Any,
     ):
         """Perform the acquisition and fill the dataset."""
-
         # Create the base dataset
         make_base_mrd(filename, sampler, phantom, sim_conf, handlers, smaps, coil_cov)
 

src/snake/core/engine/nufft.py

@@ -1,7 +1,6 @@
 """Acquisition engine using nufft."""
 
 from collections.abc import Sequence
-from copy import deepcopy
 
 import ismrmrd as mrd
 import numpy as np

src/snake/core/handlers/motion/__init__.py

@@ -1,4 +1,5 @@
-#!/usr/bin/env python3
+"""Motion Handler Module."""
+
 from .image import RandomMotionImageHandler
 
 __all__ = ["RandomMotionImageHandler"]

src/snake/core/handlers/noise.py

@@ -1,17 +1,18 @@
+"""Handler that add noise to the phantom in the image domain."""
+
 import numpy as np
+from numpy.typing import NDArray
 from copy import deepcopy
 
-from numpy.typing import NDArray
-import pandas as pd
 from functools import partial
 
-from ..._meta import LogMixin
 from ..phantom import Phantom, DynamicData
 from ..simulation import SimConfig
 from .base import AbstractHandler
 
 
-def apply_noise(phantom: Phantom, data, idx, variance) -> Phantom:
+def apply_noise(phantom: Phantom, data: NDArray, idx: int, variance: float) -> Phantom:
+    """Apply noise to the phantom at time idx."""
     rng = np.random.default_rng((int(data[0, idx]), idx))  # new seed for every frame
     noise_tissue = rng.standard_normal(size=phantom.masks.shape[1:]) * np.sqrt(variance)
     new_phantom = deepcopy(phantom)
@@ -21,12 +22,13 @@ def apply_noise(phantom: Phantom, data, idx, variance) -> Phantom:
 
 
 class NoiseHandler(AbstractHandler):
+    """Handler that add noise tot the phantom in the image domain."""
 
     __handler_name__ = "noise-image"
     variance: float
 
     def get_static(self, phantom: Phantom, sim_conf: SimConfig) -> Phantom:
-        """Add a static noise tissue"""
+        """Add a static noise tissue."""
         noise_tissue = np.ones_like(phantom.masks[0])
         noise_props = np.array([[100000, 100000, 100000, 1, 0]])
         new_phantom = phantom.add_tissue(
@@ -36,7 +38,7 @@ def get_static(self, phantom: Phantom, sim_conf: SimConfig) -> Phantom:
         return new_phantom
 
     def get_dynamic(self, phantom: Phantom, sim_conf: SimConfig) -> DynamicData:
-        """Add a dynamic noise tissue"""
+        """Add a dynamic noise tissue."""
         return DynamicData(
             "noise",
             data=np.ones((1, sim_conf.max_n_shots), dtype=np.int32) * sim_conf.rng_seed,

src/snake/core/phantom/static.py

@@ -56,7 +56,7 @@ def add_tissue(
         tissue_name: str,
         mask: NDArray[np.float32],
         props: NDArray[np.float32],
-        phantom_name=None,
+        phantom_name: str | None = None,
     ) -> Phantom:
         """Add a tissue to the phantom. Creates a new Phantom object."""
         masks = np.concatenate((self.masks, mask[None, ...]), axis=0)

src/snake/core/sampling/factories.py

@@ -104,7 +104,7 @@ def get_kspace_slice_loc(
         )
     rng = validate_rng(rng)
 
-    def _get_samples(p):
+    def _get_samples(p: NDArray) -> list[int]:
         p /= np.sum(p)
         return list(rng.choice(borders, size=n_samples_borders, replace=False, p=p))
 

src/snake/core/sampling/samplers.py

@@ -185,7 +185,6 @@ class LoadTrajectorySampler(NonCartesianAcquisitionSampler):
 
     def _single_frame(self, sim_conf: SimConfig) -> NDArray:
         """Load the trajectory."""
-
         data = read_trajectory(self.path, raster_time=self.raster_time)[0]
         data /= 0.5 * data.max()
         return data
@@ -262,7 +261,6 @@ class EPI3dAcquisitionSampler(BaseSampler):
 
     def _single_frame(self, sim_conf: SimConfig) -> NDArray:
         """Generate the sampling pattern."""
-
         return stacked_epi_factory(
             shape=sim_conf.shape,
             accelz=self.accelz,

src/snake/core/simulation.py

@@ -48,7 +48,8 @@ def _repr_html_(obj: Any, vertical: bool = True) -> str:
                 field_value_str = repr(field_value)
 
             table_rows.append(
-                f"<tr><td>{field_name} (<i>{field_type}</i>)</td><td>{field_value_str}</td></tr>"
+                f"<tr><td>{field_name}(<i>{field_type}</i>)</td>"
+                f"<td>{field_value_str}</td></tr>"
             )
     else:
         table_rows.append(

src/snake/toolkit/analysis/metrics.py

@@ -54,8 +54,10 @@ def get_snr(test: NDArray, ref: NDArray, roi: NDArray | None = None) -> float:
     return np.sqrt(np.mean(abs(signal) ** 2) / np.mean(abs(noise) ** 2))
 
 
-def get_snr_console_db(test: NDArray, roi_data=None, roi_noise=None) -> float:
-    """Compute the SNR 'like at the console'
+def get_snr_console_db(
+    test: NDArray, roi_data: NDArray = None, roi_noise: NDArray = None
+) -> float:
+    """Compute the SNR 'like at the console'.
 
     using region of interest for the signal and noise in the same image.
 

src/snake/toolkit/analysis/stats.py

@@ -7,7 +7,6 @@
 import numpy as np
 
 from nilearn.glm.first_level import make_first_level_design_matrix, run_glm
-from nilearn.glm.first_level.hemodynamic_models import _resample_regressor
 from nilearn.glm import compute_contrast, expression_to_contrast_vector
 from nilearn.glm.thresholding import fdr_threshold
 from scipy.stats import norm

src/snake/toolkit/cli/acquisition.py

@@ -5,7 +5,6 @@
 from omegaconf import OmegaConf
 from snake.core.engine import BaseAcquisitionEngine
 from snake.core.handlers import HandlerList
-from snake.mrd_utils import make_base_mrd
 from snake.core.phantom import Phantom
 from snake.core.smaps import get_smaps
 from snake.toolkit.cli.config import ConfigSNAKE, cleanup_cuda, make_hydra_cli

src/snake/toolkit/cli/config.py

@@ -5,7 +5,6 @@
 from dataclasses import dataclass, field
 import hydra
 from hydra.core.config_store import ConfigStore
-from hydra import compose, initialize
 from omegaconf import OmegaConf, DictConfig
 
 from snake.core.simulation import SimConfig
@@ -122,13 +121,8 @@ def cleanup_cuda() -> None:
     cp._default_pinned_memory_pool = cp.cuda.PinnedMemoryPool()
 
 
-def make_hydra_cli(fun):
+def make_hydra_cli(fun: callable) -> callable:
+    """Create a Hydra CLI for the function."""
     return hydra.main(
         version_base=None, config_path="../../../cli-conf", config_name="config"
     )(fun)
-
-
-def load_config(filename, *overrides):
-    with initialize(config_path="../../../cli-conf", job_name="test_app"):
-        cfg = compose(config_name="config", overrides=list(overrides))
-        return cfg

src/snake/toolkit/cli/reconstruction.py

@@ -81,7 +81,7 @@ def reconstruction(cfg: DictConfig) -> None:
     gc.collect()
 
     results = []
-    for name, rec in cfg.reconstructors.items():
+    for _name, rec in cfg.reconstructors.items():
         rec_str = str(rec)  # FIXME Also use parameters  of reconstructors
         data_rec_file = Path(f"data_rec_{rec_str}.npy").resolve()
         data_zscore_file = Path(f"data_zscore_{rec_str}.npy").resolve()

src/snake/toolkit/plotting.py

@@ -55,15 +55,21 @@ def get_axis_properties(
         cmin = max(0, cmin - arr_pad)
         cmax = min(cmax + arr_pad, mask.shape[1])
         bbox[i] = (slice(rmin, rmax), slice(cmin, cmax))
-    hdiv, vdiv = get_hdiv_vdiv(array_bg, bbox, slices, width_inches, cbar=cbar)
+    hdiv, vdiv = _get_hdiv_vdiv(array_bg, bbox, slices, width_inches, cbar=cbar)
 
     return hdiv, vdiv, tuple(bbox), slices
 
 
-def get_hdiv_vdiv(array_bg, bbox, slices, width_inches, cbar=False):
+def _get_hdiv_vdiv(
+    array_bg: NDArray,
+    bbox: tuple[tuple[slice]],
+    slices: tuple[slice],
+    width_inches: float,
+    cbar: bool = False,
+) -> tuple[NDArray, NDArray]:
     sizes = np.array([(bb.stop - bb.start) for b in bbox for bb in b])
 
-    sizes = tuple(array_bg[s][b].shape for s, b in zip(slices, bbox))
+    sizes = tuple(array_bg[s][b].shape for s, b in zip(slices, bbox, strict=False))
     alpha1 = sizes[1][1] / sizes[2][1]
     update_sizes = [[0, 0], [0, 0], [0, 0]]
     update_sizes[2][0] = sizes[2][0]
@@ -90,7 +96,7 @@ def get_hdiv_vdiv(array_bg, bbox, slices, width_inches, cbar=False):
                 0.02 * hdiv[0],
             ]
         )
-    hdivv = np.array(hdiv)
+    np.array(hdiv)
     height_inches = width_inches * aspect
     vdiv = np.array([height_inches * split_tb, height_inches * (1 - split_tb)])
     return hdiv, vdiv
@@ -113,7 +119,7 @@ def plot_frames_activ(
     bbox: tuple[Any, ...],
     z_thresh: float = 3,
     z_max: float = 11,
-    bg_cmap="gray",
+    bg_cmap: str = "gray",
 ) -> tuple[plt.Axes, matplotlib.image.AxesImage]:
     """Plot activation maps and background.
 
@@ -189,7 +195,7 @@ def axis3dcut(
             background, cuts_, width_inches, cbar=cbar
         )
     elif bbox is not None and slices is not None:
-        hdiv, vdiv = get_hdiv_vdiv(background, bbox, slices, width_inches, cbar=cbar)
+        hdiv, vdiv = _get_hdiv_vdiv(background, bbox, slices, width_inches, cbar=cbar)
         bbox_ = bbox
         slices_ = slices
     else:

src/snake/toolkit/reconstructors/__init__.py

@@ -1,4 +1,5 @@
-#!/usr/bin/env python3
+"""Reconstructors wrapping different reconstruction algorithms."""
+
 from .base import BaseReconstructor
 from .pysap import ZeroFilledReconstructor, SequentialReconstructor
 

src/snake/toolkit/reconstructors/fourier.py

@@ -1,9 +1,10 @@
 """FFT operators for MRI reconstruction."""
 
+from numpy.typing import NDArray
 import scipy as sp
 
 
-def fft(image, axis=-1):
+def fft(image: NDArray, axis: int | tuple[int] = -1) -> NDArray:
     """Apply the FFT operator.
 
     Parameters
@@ -24,8 +25,21 @@ def fft(image, axis=-1):
     )
 
 
-def ifft(kspace_data, axis=-1):
-    """Apply the inverse FFT operator."""
+def ifft(kspace_data: NDArray, axis: int | tuple[int] = -1) -> NDArray:
+    """Apply the inverse FFT operator.
+
+    Parameters
+    ----------
+    kspace_data : array
+        Image in space.
+    axis : int
+        Axis to apply the FFT.
+
+    Returns
+    -------
+    image_data : array
+        image data.
+    """
     return sp.fft.fftshift(
         sp.fft.ifftn(sp.fft.ifftshift(kspace_data, axes=axis), norm="ortho", axes=axis),
         axes=axis,