Fix python docs (#108)

* docs: fix missing space

* docs: fix collision_mesh.rest_positions not being callable in Python

* docs: fix function names in example.ipynb for ipctk 1.3.0

Author: rc

docs/source/tutorial/getting_started.rst

@@ -77,7 +77,7 @@ To start we need the current positions of the ``vertices``. For this tutorial, l
 
         .. code-block:: python
 
-            vertices = collision_mesh.rest_positions()
+            vertices = collision_mesh.rest_positions.copy()
             vertices[:, 1] *= 0.01  # Squash the bunny in the y-direction
 
 Using these deformed positions, we can build the set of active collisions.
@@ -249,7 +249,7 @@ Adaptive Barrier Stiffness
 
 The last piece of the barrier potential is the barrier stiffness. This is a weight that is multiplied by the barrier potential to better scale it relative to the energy potential. This can be a fixed value or adaptive.
 
-To compute the adaptive barrier stiffness, we can use two functions: ``initial_barrier_stiffness`` and ``update_barrier_stiffness``. The function ``initial_barrier_stiffness``computes the initial value from the current energy and barrier potential gradients. This function also provides a minimum and maximum value for the barrier stiffness. The function ``update_barrier_stiffness`` updates the barrier stiffness if the minimum distance has become too small.
+To compute the adaptive barrier stiffness, we can use two functions: ``initial_barrier_stiffness`` and ``update_barrier_stiffness``. The function ``initial_barrier_stiffness`` computes the initial value from the current energy and barrier potential gradients. This function also provides a minimum and maximum value for the barrier stiffness. The function ``update_barrier_stiffness`` updates the barrier stiffness if the minimum distance has become too small.
 
 .. md-tab-set::
 

python/example.ipynb

@@ -91058,8 +91058,8 @@
     "d = numpy.geomspace(1e-12, 0.2, 1000)\n",
     "dhat = 0.1\n",
     "b = numpy.vectorize(lambda x: barrier(x, dhat))(d)\n",
-    "b_grad = numpy.vectorize(lambda x: barrier_gradient(x, dhat))(d)\n",
-    "b_hess = numpy.vectorize(lambda x: barrier_hessian(x, dhat))(d)\n",
+    "b_grad = numpy.vectorize(lambda x: barrier_first_derivative(x, dhat))(d)\n",
+    "b_hess = numpy.vectorize(lambda x: barrier_second_derivative(x, dhat))(d)\n",
     "fig = go.Figure(data=[go.Scatter(x=d, y=b, name=\"b(x)\"), go.Scatter(x=d, y=b_grad, name=r\"\\nabla b(x)\"), go.Scatter(x=d, y=b_hess, name=r\"\\nabla^2 b(x)\")])\n",
     "fig.update_layout(yaxis_range=[-1000, 1000])\n",
     "fig.show()"