Add README.rst

README.rst

+.. image:: https://badge.fury.io/py/pyronn-torch.svg
+   :target: https://badge.fury.io/py/pyronn-torch
+   :alt: PyPI version
+
 ============
 pyronn-torch
 ============
 
+This repository provides PyTorch bindings for `PYRO-NN <https://github.com/csyben/PYRO-NN>`_ 
+
+Feel free to cite our publication:
+
+
+.. code-block:: bibtex
+
+    @article{PYRONN2019,
+    author = {Syben, Christopher and Michen, Markus and Stimpel, Bernhard and Seitz, Stephan and Ploner, Stefan and Maier, Andreas K.},
+    title = {Technical Note: PYRO-NN: Python reconstruction operators in neural networks},
+    year = {2019},
+    journal = {Medical Physics},
+    }
+
+
+Installation
+============
+
+From PyPI:
+
+.. code-block:: bash
+
+    pip install pyronn-torch
+
+From this repository:
+
+.. code-block:: bash
+
+    git clone --recurse-submodules --recursive https://github.com/theHamsta/pyronn-torch.git
+    cd pyronn-torch
+    pip install torch
+    pip install -e .
+    
+You can build a binary wheel using
+
+.. code-block:: bash
+    
+    python setup.py bdist_wheel
+
+
+Usage
+=====
+
+ 
+.. code-block:: python
+
+    #ConeBeamProjector(volume_shape,
+    #                  volume_spacing,
+    #                  volume_origin,
+    #                  projection_shape,
+    #                  projection_spacing,
+    #                  projection_origin,
+    #                  projection_matrices)
+    projector = pyronn_torch.ConeBeamProjector(
+        (128, 128, 128),
+        (2.0, 2.0, 2.0),
+        (-127.5, -127.5, -127.5),
+        (2, 480, 620),
+        [1.0, 1.0],
+        (0, 0),
+        np.array([[[-3.10e+2, -1.20e+03,  0.00e+00,  1.86e+5],
+                   [-2.40e+2,  0.00e+00,  1.20e+03,  1.44e+5],
+                   [-1.00e+00,  0.00e+00,  0.00e+00,  6.00e+2]],
+                  [[-2.89009888e+2, -1.20522754e+3, -1.02473585e-13,
+                    1.86000000e+5],
+                   [-2.39963440e+2, -4.18857765e+0,  1.20000000e+3,
+                    1.44000000e+5],
+                   [-9.99847710e-01, -1.74524058e-2,  0.00000000e+0,
+                    6.00000000e+2]]]) # two projection matrices
+    )
+    projection = projector.new_projection_tensor(requires_grad=True)
+
+    projection += 1.
+    result = projector.project_backward(projection, use_texture=with_texture)
 
-Add a short description here!
+    assert projection.requires_grad
+    assert result.requires_grad
 
+    loss = result.mean()
+    loss.backward()
 
-Description
-===========
+Or easier with `PyCONRAD <https://pypi.org/project/pyconrad/>`_ (``pip install pyconrad``)
 
-A longer description of your project goes here...
+.. code-block:: python
 
+    projector = pyronn_torch.ConeBeamProjector.from_conrad_config()
 
-Note
-====
+The configuration can then be done using `CONRAD <https://github.com/akmaier/CONRAD>`_
+(startable using ``conrad`` from command line)
 
-This project has been set up using PyScaffold 3.2.3. For details and usage
-information on PyScaffold see https://pyscaffold.org/.