Dev

.github/workflows/ruff-lint-check.yml

@@ -0,0 +1,32 @@
+name: Ruff Lint Check
+
+on:
+  push:
+    branches:
+      - main
+      - dev
+  pull_request:
+    branches:
+      - main
+      - dev
+
+jobs:
+  lint-and-test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install Ruff
+        run: |
+          pip install ruff
+
+      - name: Check Linting (Ruff)
+        run: ruff check .
+
+      - name: Check Formatting (Ruff Format)
+        run: ruff format --check .

.pre-commit-config.yaml

@@ -0,0 +1,16 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v6.0.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-toml
+      - id: check-added-large-files
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.14.14
+    hooks:
+      - id: ruff-check
+        args: [--fix]
+      - id: ruff-format

README.md

@@ -1,2 +1,135 @@
 # AngioEye
+
 AngioEye is the cohort-analysis engine for retinal Doppler holography. It browses EyeFlow .h5 outputs, reads per-segment metrics, applies QC, compares models, and aggregates results at eye/cohort level (including artery–vein summaries) to help design biomarkers. It exports clean CSV reports for stats, figures, and clinical models.
+
+---
+
+## Setup
+
+### Prerequisites
+
+- Python 3.10 or higher.
+- It is highly recommended to use a virtual environment.
+
+This project uses a `pyproject.toml` to describe all requirements needed. To start using it, **it is better to use a Python virtual environment (venv)**.
+
+```sh
+# Creates the venv
+python -m venv .venv
+
+# To enter the venv
+# If you are using Windows PowerShell, you might need to activate the "Exceution" policy
+./.venv/Scripts/activate
+```
+
+You can easily exit it with the command
+
+```sh
+deactivate
+```
+
+### 1. Basic Installation (User)
+
+```sh
+pip install -e .
+
+# Installs pipeline-specific dependencies (optional)
+pip install -e ".[pipelines]"
+```
+
+### 2. Development Setup (Contributor)
+
+```sh
+# Install all dependencies including dev tools (ruff, pre-commit, pyinstaller)
+pip install -e ".[dev,pipelines]"
+
+# Initialize pre-commit hooks (optionnal)
+pre-commit install
+```
+
+> [!NOTE]
+> The pre-commit is really usefull to run automatic checks before pushing code, reducing chances of ugly code being pushed.
+>
+> If a pre-commit hook fails, it will try to fix all needed files, **so you will need to add them again before recreating the commit**.
+
+> [!TIP]
+> You can run the linter easily, once the `dev` dependencies are installed, with the command:
+>
+> ```sh
+> # To only run the checks
+> lint-tool
+>
+> # To let the linter try to fix as much as possible
+> lint-tool --fix
+> ```
+
+---
+
+## Usage
+
+Launch the main application to process files interactively:
+
+### GUI
+
+The GUI handles batch processing for folders, single .h5/.hdf5 files, or .zip archives and lets you run multiple pipelines at once. Batch outputs are written directly into the chosen output directory (one combined `.h5` per input file).
+
+```sh
+# Via the entry point
+angioeye
+
+# Or via the script
+python src/angio_eye.py
+```
+
+### CLI
+
+The CLI is designed for batch processing in headless environments or clusters.
+
+```sh
+# Via the entry point
+angioeye-cli
+
+# Or via the script
+python src/cli.py
+```
+
+---
+
+## Pipeline System
+
+Pipelines are the heart of AngioEye. To add a new analysis, create a file in `src/pipelines/` with a class inheriting from `ProcessPipeline`.
+
+To register it to the app, add the decorator `@register_pipeline`. You can define any needed imports inside, as well as some more info.
+
+To see more complete examples, check out `src/pipelines/basic_stats.py` and `src/pipelines/dummy_heavy.py`.
+
+### Simple Pipeline Structure
+
+```python
+from pipelines import ProcessPipeline, ProcessResult, registerPipeline
+
+@registerPipeline(
+    name="My Analysis",
+    description="Calculates a custom clinical metric.",
+    required_deps=["torch>=2.2"],
+)
+class MyAnalysis(ProcessPipeline):
+    def run(self, h5file):
+        import torch
+        # 1. Read data using h5py
+        # 2. Perform calculations
+        # 3. Return metrics
+
+        metrics={"peak_flow": 12.5}
+
+        # Optional attributes applied to the pipeline group.
+        attrs = {
+            "pipeline_version": "1.0",
+            "author": "StaticExample"
+        }
+
+        return ProcessResult(
+            metrics=metrics,
+            attrs=attrs
+        )
+```

Viewer/viewer.py

@@ -1,7 +1,6 @@
 import os
 import tkinter as tk
 from tkinter import filedialog, messagebox, ttk
-from typing import Dict, List, Optional, Tuple, Union
 
 import h5py
 import numpy as np
@@ -14,11 +13,11 @@ def __init__(self) -> None:
         super().__init__()
         self.title("HDF5 Viewer")
         self.geometry("1200x800")
-        self.h5_file: Optional[h5py.File] = None
-        self.current_dataset: Optional[h5py.Dataset] = None
-        self.current_dataset_path: Optional[str] = None
-        self.axis_label_to_index: Dict[str, int] = {}
-        self.slider_vars: Dict[int, Tuple[tk.IntVar, ttk.Label]] = {}
+        self.h5_file: h5py.File | None = None
+        self.current_dataset: h5py.Dataset | None = None
+        self.current_dataset_path: str | None = None
+        self.axis_label_to_index: dict[str, int] = {}
+        self.slider_vars: dict[int, tuple[tk.IntVar, ttk.Label]] = {}
         self.colorbar = None
 
         self._build_ui()
@@ -47,8 +46,12 @@ def _build_ui(self) -> None:
         tree_frame.columnconfigure(0, weight=1)
         tree_frame.rowconfigure(0, weight=1)
 
-        self.tree = ttk.Treeview(tree_frame, columns=("path",), show="tree", selectmode="browse")
-        tree_scroll = ttk.Scrollbar(tree_frame, orient="vertical", command=self.tree.yview)
+        self.tree = ttk.Treeview(
+            tree_frame, columns=("path",), show="tree", selectmode="browse"
+        )
+        tree_scroll = ttk.Scrollbar(
+            tree_frame, orient="vertical", command=self.tree.yview
+        )
         self.tree.configure(yscrollcommand=tree_scroll.set)
         self.tree.grid(row=0, column=0, sticky="nsew")
         tree_scroll.grid(row=0, column=1, sticky="ns")
@@ -65,17 +68,23 @@ def _build_ui(self) -> None:
         self.y_axis_var = tk.StringVar()
 
         ttk.Label(axis_frame, text="X axis").grid(row=0, column=0, sticky="w")
-        self.x_combo = ttk.Combobox(axis_frame, textvariable=self.x_axis_var, state="readonly", width=24)
+        self.x_combo = ttk.Combobox(
+            axis_frame, textvariable=self.x_axis_var, state="readonly", width=24
+        )
         self.x_combo.grid(row=0, column=1, sticky="ew", padx=4, pady=2)
 
         ttk.Label(axis_frame, text="Y axis").grid(row=1, column=0, sticky="w")
-        self.y_combo = ttk.Combobox(axis_frame, textvariable=self.y_axis_var, state="readonly", width=24)
+        self.y_combo = ttk.Combobox(
+            axis_frame, textvariable=self.y_axis_var, state="readonly", width=24
+        )
         self.y_combo.grid(row=1, column=1, sticky="ew", padx=4, pady=2)
 
         self.x_combo.bind("<<ComboboxSelected>>", self.on_axis_change)
         self.y_combo.bind("<<ComboboxSelected>>", self.on_axis_change)
 
-        self.slider_frame = ttk.LabelFrame(sidebar, text="Other axes sliders", padding=8)
+        self.slider_frame = ttk.LabelFrame(
+            sidebar, text="Other axes sliders", padding=8
+        )
         self.slider_frame.grid(row=4, column=0, sticky="nsew", pady=(8, 8))
         self.slider_frame.columnconfigure(1, weight=1)
 
@@ -93,10 +102,14 @@ def _build_ui(self) -> None:
         self.canvas.get_tk_widget().grid(row=1, column=0, sticky="nsew")
 
     def _axis_label(self, axis: int) -> str:
-        size = self.current_dataset.shape[axis] if self.current_dataset is not None else "?"
+        size = (
+            self.current_dataset.shape[axis]
+            if self.current_dataset is not None
+            else "?"
+        )
         return f"Dim {axis} (size {size})"
 
-    def _selected_axis(self, label: str) -> Optional[int]:
+    def _selected_axis(self, label: str) -> int | None:
         if label == "(none)":
             return None
         return self.axis_label_to_index.get(label)
@@ -138,17 +151,32 @@ def _populate_tree(self) -> None:
         self.tree.delete(*self.tree.get_children())
         if self.h5_file is None:
             return
-        root_id = self.tree.insert("", "end", text=os.path.basename(self.h5_file.filename), open=True, values=("/"))
+        root_id = self.tree.insert(
+            "",
+            "end",
+            text=os.path.basename(self.h5_file.filename),
+            open=True,
+            values=("/"),
+        )
         self._add_tree_items(root_id, self.h5_file)
 
     def _add_tree_items(self, parent: str, obj: h5py.Group) -> None:
         for key, item in obj.items():
             if isinstance(item, h5py.Group):
-                node_id = self.tree.insert(parent, "end", text=key, open=False, values=(item.name,), tags=("group",))
+                node_id = self.tree.insert(
+                    parent,
+                    "end",
+                    text=key,
+                    open=False,
+                    values=(item.name,),
+                    tags=("group",),
+                )
                 self._add_tree_items(node_id, item)
             else:
                 label = f"{key} {item.shape}"
-                self.tree.insert(parent, "end", text=label, values=(item.name,), tags=("dataset",))
+                self.tree.insert(
+                    parent, "end", text=label, values=(item.name,), tags=("dataset",)
+                )
 
     def on_tree_select(self, _event: tk.Event) -> None:
         item_id = self.tree.focus()
@@ -166,7 +194,9 @@ def load_dataset(self, path: str) -> None:
         dataset = self.h5_file[path]
         self.current_dataset = dataset
         self.current_dataset_path = path
-        self.info_label.config(text=f"{path}\nshape={dataset.shape} dtype={dataset.dtype}")
+        self.info_label.config(
+            text=f"{path}\nshape={dataset.shape} dtype={dataset.dtype}"
+        )
 
         dims = dataset.ndim
         labels = [self._axis_label(i) for i in range(dims)]
@@ -215,7 +245,9 @@ def refresh_sliders(self) -> None:
             if axis == x_axis or axis == y_axis:
                 continue
             row = len(self.slider_vars)
-            ttk.Label(self.slider_frame, text=self._axis_label(axis)).grid(row=row, column=0, sticky="w")
+            ttk.Label(self.slider_frame, text=self._axis_label(axis)).grid(
+                row=row, column=0, sticky="w"
+            )
             var = tk.IntVar(value=0)
             scale = tk.Scale(
                 self.slider_frame,
@@ -231,7 +263,7 @@ def refresh_sliders(self) -> None:
             value_label.grid(row=row, column=2, sticky="e")
             self.slider_vars[axis] = (var, value_label)
 
-    def on_axis_change(self, _event: Optional[tk.Event] = None) -> None:
+    def on_axis_change(self, _event: tk.Event | None = None) -> None:
         if self.current_dataset is None:
             return
         self.refresh_sliders()
@@ -262,7 +294,9 @@ def update_plot(self) -> None:
         dims = ds.ndim
         x_axis = self._selected_axis(self.x_axis_var.get())
         y_axis = self._selected_axis(self.y_axis_var.get())
-        slider_indices = {axis: int(var.get()) for axis, (var, _) in self.slider_vars.items()}
+        slider_indices = {
+            axis: int(var.get()) for axis, (var, _) in self.slider_vars.items()
+        }
 
         if dims == 0:
             value = ds[()]
@@ -277,7 +311,7 @@ def update_plot(self) -> None:
             if x_axis == y_axis:
                 self._show_placeholder("Choose two different axes")
                 return
-            slices: List[Union[int, slice]] = []
+            slices: list[int | slice] = []
             for axis in range(dims):
                 if axis == x_axis or axis == y_axis:
                     slices.append(slice(None))
@@ -289,7 +323,9 @@ def update_plot(self) -> None:
                 self.ax.set_xlabel(self._axis_label(x_axis))
                 self.ax.set_ylabel(ds.name)
             else:
-                kept_axes = [idx for idx, slc in enumerate(slices) if isinstance(slc, slice)]
+                kept_axes = [
+                    idx for idx, slc in enumerate(slices) if isinstance(slc, slice)
+                ]
                 order = [kept_axes.index(y_axis), kept_axes.index(x_axis)]
                 if order != [0, 1]:
                     data = np.transpose(data, order)