adding coverage report badge

INFN-MRI · Mar 29, 2024 · 589617a · 589617a
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diff --git a/.github/workflows/style.yml b/.github/workflows/style.yml
@@ -0,0 +1,38 @@
+name: Style checking
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+  workflow_dispatch:
+
+env:
+  PYTHON_VERSION: "3.10"
+
+jobs:
+  linter-check:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Set up Python ${{ env.PYTHON_VERSION }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          cache: pip
+
+      - name: Install Python deps
+        shell: bash
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install -e .[test,dev]
+
+      - name: Black Check
+        shell: bash
+        run: black . --diff --color --check
+
+      - name: ruff Check
+        shell: bash
+        run: ruff src
diff --git a/_README.md b/_README.md
@@ -1,5 +1,6 @@
 <p align="left">
 <a href="https://www.codefactor.io/repository/github/infn-predator/deep-mr"><img src="https://www.codefactor.io/repository/github/infn-predator/deep-mr/badge" alt="CodeFactor" /></a>
+<a href="https://codecov.io/gh/INFN-PREDATOR/deep-mr" ><img src="https://codecov.io/gh/INFN-PREDATOR/deep-mr/graph/badge.svg?token=OCVA8NGK0E"/></a>
 </p>
 A framework to accelerate the development of modern MR acquisition and reconstruction methods, focused on quantitative MRI.
 

diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -3,4 +3,5 @@ myst-nb
 myst-parser
 sphinx==6.2.1 
 sphinx-autoapi 
-sphinx-book-theme 
+sphinx-book-theme
+sphinx-gallery 
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -29,10 +29,18 @@
     "sphinx.ext.autosummary",
     "sphinx.ext.napoleon",
     "sphinx.ext.viewcode",
+    "sphinx.ext.intersphinx",
+    "sphinx_gallery.gen_gallery",
 ]
 autosummary_generate = True
 autosummary_imported_members = True
 
+intersphinx_mapping = {
+    "numpy": ("http://docs.scipy.org/doc/numpy/", None),
+    "torch": ("https://docs.pytorch.org/2.0/", None),
+    "python": ("https://docs.python.org/3.4", None),
+}
+
 autodoc_mock_imports = [
     "dacite",
     "deepinv",
@@ -56,6 +64,27 @@
 # This pattern also affects html_static_path and html_extra_path.
 exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
+sphinx_gallery_conf = {
+    "examples_dirs": ["../../tutorials/"],
+    "gallery_dirs": "auto_examples",  # path to where to save gallery generated output
+    "filename_pattern": "/demo_",
+    "run_stale_examples": True,
+    "ignore_pattern": r"__init__\.py",
+    "reference_url": {
+        # The module you locally document uses None
+        "sphinx_gallery": None
+    },
+    # directory where function/class granular galleries are stored
+    "backreferences_dir": "gen_modules/backreferences",
+    # Modules for which function/class level galleries are created. In
+    # this case sphinx_gallery and numpy in a tuple of strings.
+    "doc_module": ("deepmr"),
+    # objects to exclude from implicit backreferences. The default option
+    # is an empty set, i.e. exclude nothing.
+    "exclude_implicit_doc": {},
+    "nested_sections": False,
+}
+
 # -- Options for HTML output -------------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for

diff --git a/docs/source/core/prox.md b/docs/source/core/prox.md
@@ -12,7 +12,7 @@ iterative reconstructions as regularizars (aka, PnP reconstruction).
 DeepMR expose denoisers both as ``torch.nn`` objects (to be chained in DL-based reconstructions)
 and functional forms (e.g., for simple standalone denoising).
 
-Currently available denoisers are Wavelet (``WaveletDenoiser``, ``WaveletDictDenoiser`),
+Currently available denoisers are Wavelet (``WaveletDenoiser``, ``WaveletDictDenoiser``),
 Local Low Rank (``LLRDenoiser``) and Total (Generalized) Variation (``TVDenoiser``, ``TGVDenoiser`).
 
 Both ``WaveletDenoiser`` / ``WaveletDictDenoiser`` and ``TVDenoiser`` / ``TGVDenoiser`` are adapted for complex-value inputs from the corresponding [DeepInverse](https://deepinv.github.io/deepinv/) implementations.

diff --git a/tutorials/README.md b/tutorials/README.md
diff --git a/tutorials/basics/demo_blochsim.py b/tutorials/basics/demo_blochsim.py
diff --git a/tutorials/basics/demo_custom_blochsim.py b/tutorials/basics/demo_custom_blochsim.py
diff --git a/tutorials/basics/demo_custom_linops.py b/tutorials/basics/demo_custom_linops.py
diff --git a/tutorials/basics/demo_custom_nlops.py b/tutorials/basics/demo_custom_nlops.py
diff --git a/tutorials/basics/demo_custom_qmap.py b/tutorials/basics/demo_custom_qmap.py
diff --git a/tutorials/basics/demo_data_format.py b/tutorials/basics/demo_data_format.py
@@ -0,0 +1,9 @@
+"""
+===========
+Data Format
+===========
+
+Overview of DeepMR data format.
+
+"""
+
diff --git a/tutorials/basics/demo_linops.py b/tutorials/basics/demo_linops.py
diff --git a/tutorials/basics/demo_nlops.py b/tutorials/basics/demo_nlops.py
diff --git a/tutorials/basics/demo_qmap.py b/tutorials/basics/demo_qmap.py
diff --git a/tutorials/basics/demo_signal_processing.py b/tutorials/basics/demo_signal_processing.py
diff --git a/tutorials/basics/demo_vobj.py b/tutorials/basics/demo_vobj.py