Merge pull request #108 from juriarte62/main

add jupyter notebook for denoising

fix ci issues, add pages deployment ci [WIP]

.github/workflows/pages.yml

@@ -0,0 +1,18 @@
+name: Deploy Sphinx documentation to Pages
+
+on:
+  push:
+    branches: [master] # branch to trigger deployment
+
+jobs:
+  pages:
+    runs-on: ubuntu-latest
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    permissions:
+      pages: write
+      id-token: write
+    steps:
+      - id: deployment
+        uses: sphinx-notes/pages@v3

.github/workflows/tests.yml

@@ -18,7 +18,6 @@ jobs:
           - { python: "3.12", os: "ubuntu-latest", session: "tests" }
           - { python: "3.12", os: "ubuntu-latest", session: "typeguard" }
           - { python: "3.12", os: "ubuntu-latest", session: "xdoctest" }
-          - { python: "3.12", os: "ubuntu-latest", session: "docs-build" }
           - { python: "3.11", os: "ubuntu-latest", session: "mypy" }
           - { python: "3.11", os: "ubuntu-latest", session: "tests" }
           - { python: "3.11", os: "ubuntu-latest", session: "xdoctest" }
@@ -46,8 +45,8 @@ jobs:
       - name: Check out the repository
         uses: actions/checkout@v4
 
-      - name: Set up R if not on Windows
-        if: matrix.os != 'windows-latest'
+      - name: Set up R if on MacOS
+        if: matrix.os == 'macos-latest'
         uses: r-lib/actions/setup-r@v2
 
       - name: Install remotes R package if on MacOS
@@ -128,14 +127,14 @@ jobs:
           include-hidden-files: true
 
       - name: Upload documentation
-        if: matrix.session == 'docs-build'
+        if: matrix.session == 'docs-build' && matrix.os == 'macos-latest'
         uses: actions/upload-artifact@v3
         with:
           name: docs
           path: docs/_build
 
   coverage:
-    runs-on: ubuntu-latest
+    runs-on: macos-latest
     needs: tests
     steps:
       - name: Check out the repository

docs/index.md

@@ -30,4 +30,5 @@ caption: Notebooks:
 ---
 
 notebooks/basics
+notebooks/denoise
 ```

docs/notebooks/denoise.py

@@ -0,0 +1,145 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: dynamicpet-9lkmTgRy-py3.12
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # Denoising
+
+# %% [markdown]
+# This notebook illustrates 4-D image denoising with [_Dynamic PET_].
+#
+# First, we download a 4-D PET image with its [PET-BIDS] json sidecar from
+# [OpenNeuro](https://openneuro.org/):
+#
+# [_Dynamic PET_]: https://github.com/bilgelm/dynamicpet
+# [PET-BIDS]: https://bids-specification.readthedocs.io/en/stable/modality-specific-files/positron-emission-tomography.html
+
+# %%
+from pathlib import Path
+
+import requests
+
+
+outdir = Path.cwd() / "nb_data"
+outdir.mkdir(exist_ok=True)
+
+petjson_fname = outdir / "pet_to_denoise.json"
+pet_fname = outdir / "pet_to_denoise.nii.gz"
+
+# we will download the PET for the baseline session for subject 01 from
+# https://openneuro.org/datasets/ds001420/versions/1.2.0
+baseurl = "https://s3.amazonaws.com/openneuro.org/ds001420/sub-01/ses-baseline/"
+
+peturl = (
+    baseurl
+    + "pet/sub-01_ses-baseline_pet.nii.gz"
+    + "?versionId=8Qon4IjB8ejnZq7JgCUlFJhLUYSG1zJB"
+)
+
+if not petjson_fname.exists():
+    r = requests.get(
+        baseurl
+        + "pet/sub-01_ses-baseline_pet.json"
+        + "?versionId=rLpwPCPOzgW1MduO53VKxsKGuD5K0j5R",
+        timeout=10,
+    )
+    r.raise_for_status()
+    with open(petjson_fname, "wb") as f:
+        f.write(r.content)
+
+if not pet_fname.exists():
+    with requests.get(peturl, timeout=10, stream=True) as r:
+        r.raise_for_status()
+        with open(pet_fname, "wb") as f:
+            for chunk in r.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+# %% [markdown]
+# ## Denoise
+#
+# We read in a PET image and apply HYPR-LR denoising.
+
+# %%
+from dynamicpet.denoise.hypr import hypr_lr
+from dynamicpet.petbids.petbidsimage import load
+
+
+pet = load(pet_fname)
+pet_hyprlr = hypr_lr(pet, fwhm=5)
+
+# %% [markdown]
+# ## Visualize result
+#
+# We inspect the middle time frame without and with HYPR-LR denoising.
+
+# %%
+from nilearn.image import index_img
+from nilearn.plotting import plot_anat
+
+
+# get mid slice index (slice 16)
+slice_index = pet.num_frames // 2
+
+# pick common colorbar limits
+vmin = 0
+vmax = 1.2e5
+
+plot_anat(
+    index_img(pet.img, slice_index),
+    title="4-D PET image",
+    colorbar=True,
+    draw_cross=False,
+    vmin=vmin,
+    vmax=vmax,
+)
+
+# plot HYPR-LR denoised image
+plot_anat(
+    index_img(pet_hyprlr.img, slice_index),
+    title="HYPR-LR denoised PET image",
+    colorbar=True,
+    draw_cross=False,
+    vmin=vmin,
+    vmax=vmax,
+);
+
+# %% [markdown]
+# We can also look at the time activity curve for a single voxel.
+
+# %%
+import matplotlib.pyplot as plt
+
+
+voxel_index = (100, 100, 100)  # an arbitrarily selected voxel
+
+time = pet.frame_mid
+pet_tac = pet.dataobj[*voxel_index, ...]
+pet_hyprlr_tac = pet_hyprlr.dataobj[*voxel_index, ...]
+
+plt.figure()
+plt.plot(time, pet_tac, label="Without denoising")
+plt.plot(time, pet_hyprlr_tac, label="HYPR-LR")
+plt.xlabel("Time (minutes)")
+plt.ylabel(f'Radioactivity ({pet.json_dict["Units"]})')
+plt.title("Time activity curve (TAC) for a single voxel")
+plt.legend();
+
+# %% [markdown]
+# ## Command line interface
+#
+# Instead of using the Python API, we can also perform denoising via the
+# command line:
+
+# %%
+# !denoise --method HYPRLR --fwhm 5 --outputdir nb_data nb_data/pet_to_denoise.nii.gz