update loop docs and apis (#261)

update loop docs and apis

brainpy/dyn/base.py

@@ -1289,7 +1289,7 @@ def __init__(
                                      for _ in range(v.batch_axis - len(self.index) + 1)])))
       else:
         index = self.index
-      self.slice_vars[k] = bm.VariableRef(v, index)
+      self.slice_vars[k] = bm.VariableView(v, index)
 
     # sub-nodes
     nodes = target.nodes(method='relative', level=1, include_self=False).subset(DynamicalSystem)

brainpy/math/controls.py

@@ -785,6 +785,10 @@ def _body_fun(op):
     if not isinstance(static_vals, (tuple, list)):
       static_vals = (static_vals, )
     new_vals = body_fun(*static_vals)
+    if new_vals is None:
+      new_vals = tuple()
+    if not isinstance(new_vals, tuple):
+      new_vals = (new_vals, )
     return [v.value for v in dyn_vars], new_vals
 
   def _cond_fun(op):

brainpy/math/jaxarray.py

@@ -16,7 +16,7 @@
   'Variable',
   'TrainVar',
   'Parameter',
-  'VariableRef',
+  'VariableView',
 ]
 
 # Ways to change values in a zero-dimensional array
@@ -1494,14 +1494,20 @@ def __init__(self, value, dtype=None, batch_axis: int = None):
                      lambda aux_data, flat_contents: Parameter(*flat_contents))
 
 
-class VariableRef(Variable):
-  """A reference of Variable instance."""
+class VariableView(Variable):
+  """A view of a Variable instance.
+
+  This class is used to create a slice view of ``brainpy.math.Variable``.
+
+  ``VariableView`` can be used to update the subset of the original
+  Variable instance, and make operations on this subset of the Variable.
+  """
   def __init__(self, value: Variable, index):
     self.index = index
     if not isinstance(value, Variable):
       raise ValueError('Must be instance of Variable.')
     temp_shape = tuple([1] * len(index))
-    super(VariableRef, self).__init__(jnp.zeros(temp_shape), batch_axis=value.batch_axis)
+    super(VariableView, self).__init__(jnp.zeros(temp_shape), batch_axis=value.batch_axis)
     self._value = value
 
   @property

docs/tutorial_building/build_conductance_neurons.ipynb

@@ -36,9 +36,7 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
+   "cell_type": "markdown",
    "source": [
     "On the other hand, simplified models do not care about the physiological features of neurons but mainly focus on how to reproduce the exact spike timing. Therefore, they are more simplified and maybe not biologically explicable.\n",
     "\n",
@@ -47,21 +45,19 @@
    "metadata": {
     "collapsed": false,
     "pycharm": {
-     "name": "#%%\n"
+     "name": "#%% md\n"
     }
    }
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
+   "cell_type": "markdown",
    "source": [
     "## Building an ion channel"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
-     "name": "#%%\n"
+     "name": "#%% md\n"
     }
    }
   },
@@ -436,17 +432,6 @@
    "source": [
     "By combining different ion channels, we can get different types of conductance-based neuron models easily and straightforwardly. To see all predifined channel models in BrainPy, please click [here](../apis/dyn.rst)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {