Move dev/unused dependencies to optional extras

.github/workflows/ci.yml

@@ -0,0 +1,32 @@
+name: CI
+
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: uv sync --extra dev
+
+      - name: Lint
+        run: uv run ruff check src/ scripts/qa/
+
+      - name: Test
+        run: uv run pytest tests/ -q

.gitignore

@@ -72,3 +72,6 @@ private
 
 # NFS hanging stuff
 .nfs*
+
+# Node
+node_modules/

pyproject.toml

@@ -13,13 +13,12 @@ authors = [
 ]
 readme = "README.md"
 license = {file = "LICENSE"}
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: BSD License",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
@@ -34,26 +33,18 @@ dependencies = [
     "nibabel>=3.0.0",
     "nilearn>=0.6.0",
     "scikit-learn>=0.22.0",
-    "jupyterlab>=3.0.0",
-    "ipywidgets>=7.5.0",
-    "h5py>=2.10.0",
     "joblib>=1.0.0",
-    "pillow>=7.0.0",
-    "lxml>=4.5.0",
-    "ruff>=0.12.12",
     "tqdm>=4.67.1",
     "jinja2>=3.0.0",
     "pyyaml>=6.0",
     "pybids>=0.16.0",
     "pycortex>=1.0.0",
-    "pytest>=8.4.2",
-    "pydeface>=2.0.2",
 ]
 
 [project.optional-dependencies]
 dev = [
-    "pytest>=6.0",
-    "ruff>=0.1.0",
+    "pytest>=8.4.2",
+    "ruff>=0.12.12",
 ]
 
 [project.urls]
@@ -71,7 +62,7 @@ hyperface = ["assets/*.yaml", "templates/*.html"]
 
 [tool.ruff]
 line-length = 88
-target-version = "py39"
+target-version = "py310"
 # Exclude archival code that should not be modified
 exclude = [
     "scripts/presentation/**",  # Legacy Python 2 stimulus presentation code

scripts/qa/print-tsnr-summary.py

@@ -85,14 +85,18 @@ def format_task_summary(task_stats: dict) -> str:
         f"  Number of subjects: {n_subjects}",
         "",
         "  tSNR (temporal signal-to-noise ratio):",
-        f"    Mean across subjects: {np.mean(mean_tsnrs):.1f} ± {np.std(mean_tsnrs):.1f}",
+        f"    Mean across subjects: {np.mean(mean_tsnrs):.1f}"
+        f" ± {np.std(mean_tsnrs):.1f}",
         f"    Median across subjects: {np.median(mean_tsnrs):.1f}",
         f"    Min: {np.min(mean_tsnrs):.1f}",
         f"    Max: {np.max(mean_tsnrs):.1f}",
         "",
         "  Paper-ready text:",
-        f"    The mean tSNR across subjects was {np.mean(mean_tsnrs):.1f} ± {np.std(mean_tsnrs):.1f} "
-        f"(median {np.median(mean_tsnrs):.1f}, min {np.min(mean_tsnrs):.1f}, max {np.max(mean_tsnrs):.1f}).",
+        f"    The mean tSNR across subjects was "
+        f"{np.mean(mean_tsnrs):.1f} ± {np.std(mean_tsnrs):.1f} "
+        f"(median {np.median(mean_tsnrs):.1f}, "
+        f"min {np.min(mean_tsnrs):.1f}, "
+        f"max {np.max(mean_tsnrs):.1f}).",
         "",
     ]
 

scripts/qa/qa-plot-accuracy.py

@@ -134,7 +134,7 @@ def plot_accuracy_figure(
 
     # Bar plot for mean accuracy
     x_positions = np.arange(len(subjects))
-    bars = ax.bar(
+    ax.bar(
         x_positions,
         mean_accuracies,
         color=PRIMARY_COLOR,
@@ -145,7 +145,7 @@ def plot_accuracy_figure(
     )
 
     # Scatter plot for individual run values
-    for i, (x_pos, values) in enumerate(zip(x_positions, all_run_values)):
+    for x_pos, values in zip(x_positions, all_run_values, strict=True):
         jitter = np.random.uniform(-0.15, 0.15, len(values))
         ax.scatter(
             [x_pos + j for j in jitter],
@@ -205,7 +205,7 @@ def format_accuracy_summary(accuracy_data: dict[str, dict[str, int]]) -> str:
     subject_means = []
     perfect_subjects = 0
 
-    for subject, runs in accuracy_data.items():
+    for _subject, runs in accuracy_data.items():
         values = list(runs.values())
         all_values.extend(values)
         subject_means.append(np.mean(values))
@@ -247,7 +247,8 @@ def format_accuracy_summary(accuracy_data: dict[str, dict[str, int]]) -> str:
             "",
             "Paper-ready text:",
             f"  Participants achieved a mean accuracy of {np.mean(subject_means):.1f}% "
-            f"(median {np.median(subject_means):.1f}%, min {np.min(subject_means):.1f}%, "
+            f"(median {np.median(subject_means):.1f}%, "
+            f"min {np.min(subject_means):.1f}%, "
             f"max {np.max(subject_means):.1f}%) on the visual memory task. "
             f"{perfect_subjects} out of {n_subjects} participants achieved "
             f"100% accuracy across all runs.",

scripts/qa/qa-plot-isc.py

@@ -101,7 +101,7 @@ def print_summary_stats(isc_data: list[np.ndarray], subject_ids: list[str]) -> N
     print(header)
     print("-" * 60)
 
-    for subject_id, isc in zip(subject_ids, isc_data):
+    for subject_id, isc in zip(subject_ids, isc_data, strict=True):
         valid = isc[np.isfinite(isc)]
         vals = [valid.mean(), np.median(valid), valid.std(), valid.min(), valid.max()]
         row = f"{subject_id:<15} " + " ".join(f"{v:>8.4f}" for v in vals)
@@ -173,7 +173,7 @@ def main():
 
     # Create individual subject surface plots
     print("\nCreating individual subject surface plots...")
-    for subject_id, isc in zip(subject_ids, isc_data):
+    for subject_id, isc in zip(subject_ids, isc_data, strict=True):
         subject_path = figures_dir / f"{subject_id}_desc-isc_{plot_type}.png"
         create_fsaverage6_plot(
             isc,

scripts/qa/qa-plot-motion.py

@@ -219,7 +219,7 @@ def create_fd_violin_plots_by_session(
     print("  Creating FD violin plots by session...")
 
     session_data: dict[str | None, list[str]] = {}
-    for confounds_file, session in zip(confounds_files, sessions):
+    for confounds_file, session in zip(confounds_files, sessions, strict=True):
         session_data.setdefault(session, []).append(confounds_file)
 
     for session, session_files in session_data.items():

scripts/qa/qa-plot-participant-datasets.py

@@ -29,7 +29,6 @@ def main():
 
     # Create binary matrix
     n_participants = len(df)
-    datasets = ["hyperface", "budapest", "identity_decoding"]
     matrix = np.zeros((n_participants, 3), dtype=int)
 
     # All participants have hyperface

scripts/qa/qa-save-isc.py

@@ -65,7 +65,7 @@ def load_and_process_subject(
     events = load_events(subject, task="visualmemory", data_dir=data_dir)
 
     processed_runs = []
-    for data, evt in zip(responses, events):
+    for data, evt in zip(responses, events, strict=True):
         n_trs = data.shape[0]
         clip_mask = get_clip_tr_mask(evt, n_trs, tr=tr)
         data_clips = data[clip_mask]
@@ -111,7 +111,7 @@ def main():
     n_trs_list = [d.shape[0] for d in subjects_data]
     if len(set(n_trs_list)) > 1:
         print(f"\nERROR: Inconsistent TR counts: {set(n_trs_list)}")
-        for subj_id, n_trs in zip(subject_ids, n_trs_list):
+        for subj_id, n_trs in zip(subject_ids, n_trs_list, strict=True):
             print(f"  {subj_id}: {n_trs}")
         return 1
 

scripts/qa/stimuli/plot-behavioral-ratings.py

@@ -90,7 +90,7 @@ def plot_factor_proportions(df, output_path):
 
     total_n = len(df)
 
-    for ax, (factor, config) in zip(axes, FACTOR_CONFIG.items()):
+    for ax, (factor, config) in zip(axes, FACTOR_CONFIG.items(), strict=True):
         # Get counts in specified order
         counts = df[factor].value_counts()
         order = [lvl for lvl in config["order"] if lvl in counts.index]
@@ -106,7 +106,7 @@ def plot_factor_proportions(df, output_path):
         )
 
         # Add percentage labels above bars
-        for i, (count, bar) in enumerate(zip(counts.values, bars)):
+        for count, bar in zip(counts.values, bars, strict=True):
             pct = 100 * count / total_n
             ax.text(
                 bar.get_x() + bar.get_width() / 2,

src/hyperface/io.py

@@ -113,17 +113,10 @@ def load_run_order_config() -> dict:
     FileNotFoundError
         If config file not found in package
     """
-    try:
-        # Python 3.9+
-        config_file = resources.files("hyperface.assets").joinpath(
-            "visualmemory_run_order.yaml"
-        )
-        config_text = config_file.read_text()
-    except AttributeError:
-        # Python 3.8 fallback
-        config_filename = "visualmemory_run_order.yaml"
-        with resources.open_text("hyperface.assets", config_filename) as f:
-            config_text = f.read()
+    config_file = resources.files("hyperface.assets").joinpath(
+        "visualmemory_run_order.yaml"
+    )
+    config_text = config_file.read_text()
 
     return yaml.safe_load(config_text)
 

src/hyperface/utils.py

@@ -33,7 +33,7 @@ def compute_tsnr(data: np.ndarray, confounds: pd.DataFrame) -> np.ndarray:
     data : array of shape (dim1, dim2, dim3, n_volumes)
         EPI data
     confounds : pandas Dataframe
-        dataframe containing confounds generated by fmriprepp
+        dataframe containing confounds generated by fmriprep
 
     Returns
     ------
@@ -90,7 +90,7 @@ def clean_data(data: np.ndarray, confounds: pd.DataFrame) -> np.ndarray:
     data : array of shape (n_volumes, n_features)
         flattened EPI data
     confounds : pandas Dataframe
-        dataframe containing confounds generated by fmriprepp
+        dataframe containing confounds generated by fmriprep
 
     Returns
     -------