Tune README.rst, index.rst

README.rst

@@ -36,7 +36,7 @@ Usage
 
 Create a :class:`pystencils.AssignmentCollection` with pystencils:
 
-.. testcode::
+.. code-block:: python
 
     import sympy
     import pystencils
@@ -50,8 +50,7 @@ Create a :class:`pystencils.AssignmentCollection` with pystencils:
     print(forward_assignments)
 
 
-.. testoutput::
-    :options: -ELLIPSIS, +NORMALIZE_WHITESPACE
+.. code-block:: python
 
     Subexpressions:
     Main Assignments:
@@ -59,7 +58,7 @@ Create a :class:`pystencils.AssignmentCollection` with pystencils:
    
 You can then obtain the corresponding backward assignments:
 
-.. testcode::
+.. code-block:: python
 
     from pystencils.autodiff import AutoDiffOp, create_backward_assignments
     backward_assignments = create_backward_assignments(forward_assignments)
@@ -68,7 +67,7 @@ You can then obtain the corresponding backward assignments:
 
 You can see the derivatives with respective to the two inputs multiplied by the gradient `diffz_C` of the output `z_C`.
 
-.. testoutput::
+.. code-block:: python
     :options: -ELLIPSIS, +NORMALIZE_WHITESPACE
 
     Subexpressions:
@@ -78,7 +77,7 @@ You can see the derivatives with respective to the two inputs multiplied by the
 
 You can also use the class :class:`.AutoDiffOp` to obtain both the assignments (if you are curious) and auto-differentiable operations for Tensorflow...
 
-.. testcode::
+.. code-block:: python
 
     op = AutoDiffOp(forward_assignments)
     backward_assignments = op.backward_assignments   
@@ -89,7 +88,7 @@ You can also use the class :class:`.AutoDiffOp` to obtain both the assignments (
 
 ... or Torch:
 
-.. testcode::
+.. code-block:: python
 
     x_tensor = pystencils.autodiff.torch_tensor_from_field(x, cuda=False, requires_grad=True)
     y_tensor = pystencils.autodiff.torch_tensor_from_field(y, cuda=False, requires_grad=True)

docs/index.rst

@@ -4,7 +4,89 @@ pystencils-autodiff
 
 This is the documentation of **pystencils-autodiff**.
 
-.. include:: ../README.rst
+Installation
+------------
+
+Install via pip :
+
+.. code-block:: bash
+
+   pip install pystencils-autodiff
+
+or if you downloaded this `repository <https://github.com/theHamsta/pystencils_autodiff>`_ using:
+
+.. code-block:: bash
+
+   pip install -e .
+
+
+Usage
+-----
+
+Create a :class:`pystencils.AssignmentCollection` with pystencils:
+
+.. testcode::
+
+    import sympy
+    import pystencils
+
+    z, x, y = pystencils.fields("z, y, x: [20,30]")
+
+    forward_assignments = pystencils.AssignmentCollection({
+        z[0, 0]: x[0, 0] * sympy.log(x[0, 0] * y[0, 0])
+    })
+
+    print(forward_assignments)
+
+
+.. testoutput::
+    :options: -ELLIPSIS, +NORMALIZE_WHITESPACE
+
+    Subexpressions:
+    Main Assignments:
+         z[0,0] ← y_C*log(x_C*y_C)
+   
+You can then obtain the corresponding backward assignments:
+
+.. testcode::
+
+    from pystencils.autodiff import AutoDiffOp, create_backward_assignments
+    backward_assignments = create_backward_assignments(forward_assignments)
+
+    # Sorting for reprducible outputs
+    backward_assignments.main_assignments = sorted(backward_assignments.main_assignments, key=lambda a: str(a))
+
+    print(backward_assignments)
+
+You can see the derivatives with respective to the two inputs multiplied by the gradient `diffz_C` of the output `z_C`.
+
+.. testoutput::
+    :options: -ELLIPSIS, +NORMALIZE_WHITESPACE
+
+    Subexpressions:
+    Main Assignments:
+        \hat{x}[0,0] ← diffz_C*y_C/x_C
+        \hat{y}[0,0] ← diffz_C*(log(x_C*y_C) + 1)
+
+You can also use the class :class:`.AutoDiffOp` to obtain both the assignments (if you are curious) and auto-differentiable operations for Tensorflow...
+
+.. testcode::
+
+    op = AutoDiffOp(forward_assignments)
+    backward_assignments = op.backward_assignments   
+
+    x_tensor = pystencils.autodiff.tf_variable_from_field(x)
+    y_tensor = pystencils.autodiff.tf_variable_from_field(y)
+    tensorflow_op = op.create_tensorflow_op({x: x_tensor, y: y_tensor}, backend='tensorflow')
+
+... or Torch:
+
+.. testcode::
+
+    x_tensor = pystencils.autodiff.torch_tensor_from_field(x, cuda=False, requires_grad=True)
+    y_tensor = pystencils.autodiff.torch_tensor_from_field(y, cuda=False, requires_grad=True)
+
+    z_tensor = op.create_tensorflow_op({x: x_tensor, y: y_tensor}, backend='torch')
 
 
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