Add tutorials

docs/colab.rst

@@ -0,0 +1,65 @@
+.. highlight:: shell
+
+============================
+Use DIRECT with Google Colab
+============================
+
+
+
+1. First mount your Google drive in Colab and create a directory named, e.g. `DIRECT`, and `cd` there:
+
+.. code-block:: ipython3
+
+    %cd /content/drive/MyDrive/DIRECT/
+
+This `notebook <https://colab.research.google.com/notebooks/io.ipynb>`_ can help with mounting your Google drive.
+
+
+2. Clone the repo:
+
+.. code-block:: ipython3
+
+    !git clone https://github.com/NKI-AI/direct.git
+
+3. Copy paste and run the following
+
+.. code-block:: ipython3
+
+    !wget -O mini.sh https://repo.anaconda.com/miniconda/Miniconda3-py38_4.8.2-Linux-x86_64.sh
+    !chmod +x mini.sh
+    !bash ./mini.sh -b -f -p /usr/local
+    !conda install -q -y jupyter
+    !conda install -q -y google-colab -c conda-forge
+    !python -m ipykernel install --name "py38" --user
+
+The above block is needed to install python 3.8 in Colab as it runs using Python 3.7.
+
+4. Run the following to install the latest `PyTorch` version:
+
+.. code-block:: ipython3
+
+    !pip3 uninstall torch
+    !pip3 uninstall torchvision
+    !pip3 uninstall torchaudio
+    !pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
+
+5.  Navigate to the repo:
+
+.. code-block:: ipython3
+
+    %cd direct/
+
+6. Install package.
+
+.. code-block:: ipython3
+
+    !python3 setup.py install
+
+OR
+
+.. code-block:: ipython3
+
+    !python3 -m pip install -e ".[dev]"
+
+7. Run experiments using the configuration files in the `projects <https://github.com/NKI-AI/direct/tree/main/projects>`_ folder,
+or you can set up your own configuration files following our `template <https://docs.aiforoncology.nl/direct/config.html>`_.

docs/examples.rst

@@ -0,0 +1 @@
+.. include:: ./examples/calgary_campinas_dataset.rst

docs/examples/calgary_campinas_dataset.rst

@@ -0,0 +1,99 @@
+=======================================
+Setting up the Calgary-Campinas Dataset
+=======================================
+
+Imports
+-------
+
+.. code:: ipython3
+
+  from functools import partial
+
+  import numpy as np
+  import matplotlib.pyplot as plt
+
+
+  from direct.data.datasets import CalgaryCampinasDataset
+  from direct.data.mri_transforms import build_mri_transforms
+  from direct.data.transforms import fft2, ifft2, modulus, root_sum_of_squares
+  from direct.common.subsample import CalgaryCampinasMaskFunc
+
+Define forward and backward transforms
+--------------------------------------
+
+
+.. code:: ipython3
+
+  forward_operator = partial(fft2, dim=(1,2), centered=False)
+  backward_operator = partial(ifft2, dim=(1,2), centered=False)
+
+Define sub-sampling function
+----------------------------
+
+.. code:: ipython3
+
+  mask_func = CalgaryCampinasMaskFunc(accelerations=[5, 10])
+
+Define MRI transforms
+---------------------
+
+.. code:: ipython3
+
+  transforms = build_mri_transforms(
+      forward_operator=forward_operator,
+      backward_operator=backward_operator,
+      mask_func=mask_func,
+      scaling_key="masked_kspace",
+  )
+
+Create the Dataset
+------------------
+
+.. code:: ipython3
+
+  # root pointing to the Calgary-Campinas data directory
+  root = "<...>"
+  dataset = CalgaryCampinasDataset(
+      root = root,
+      transform=transforms,
+      crop_outer_slices=True,
+  )
+
+Investigate a sample
+--------------------
+
+.. code:: ipython3
+
+  sample = dataset[20]
+
+  print("Sample keys: ", sample.keys())
+
+Visualizations
+~~~~~~~~~~~~~~
+
+.. code:: ipython3
+
+  plt.imshow(sample["target"], cmap='gray')
+  plt.axis("off")
+  plt.title("Target")
+  plt.show()
+
+  plt.imshow(sample["sampling_mask"].squeeze(), cmap='gray')
+  plt.axis("off")
+  plt.title("Sub-Sampling Mask")
+  plt.show()
+
+.. code:: ipython3
+
+  masked_kspace = sample["masked_kspace"]
+  print("K-space shape: (Coils, Height, Width, Complex) = ", tuple(masked_kspace.shape))
+
+  backprojected_kspace = backward_operator(masked_kspace)
+
+  fig, axs = plt.subplots(1, masked_kspace.shape[0], figsize=(20,50))
+
+  for i in range(masked_kspace.shape[0]):
+      axs[i].imshow(modulus(backprojected_kspace[i]))
+      axs[i].axis("off")
+      axs[i].set_title(f"Coil {i+1}")
+  plt.show()

docs/index.rst

@@ -36,6 +36,13 @@ images such as MRIs from partially observed or noisy input data.
    datasets
    samplers
 
+.. toctree::
+   :maxdepth: 1
+   :caption: Examples
+
+   examples.rst
+   colab.rst
+
 .. toctree::
    :maxdepth: 1
    :caption: Model Zoo