diff --git a/docs/user/py/classes/index.rst b/docs/user/py/classes/index.rst
index fa64a75bf241d10d51b49e9ce586a6e9a456f571..b6529f342e8dcff34a036a18f5aeb3f52815af21 100644
--- a/docs/user/py/classes/index.rst
+++ b/docs/user/py/classes/index.rst
@@ -13,43 +13,94 @@ Alphabetical listing of moose classes
.. py:class:: Adaptor
- This is the adaptor class. It is used in interfacing different kinds of solver with each other, especially for electrical to chemical signeur models. The Adaptor class is the core of the API for interfacing between different solution engines. It is currently in use for interfacing between chemical and electrical simulations, but could be used for other cases such as mechanical models. The API for interfacing between solution engines rests on the following capabilities of MOOSE:
- 1. The object-oriented interface with classes mapped to biological and modeling concepts such as electrical and chemical compartments, ion channels and molecular pools.
- 2. The invisible mapping of Solvers (Objects implementing numerical engines) to the object-oriented interface. Solvers work behind the scenes to update the objects.
- 3. The messaging interface which allows any visible field to be accessed and updated from any other object.
- 4. The clock-based scheduler which drives operations of any subset of objects at any interval. For example, this permits the operations of field access and update to take place at quite different timescales from the numerical engines.
- 5. The implementation of Adaptor classes. These perform a linear transformation::
- (y = scale * (x + inputOffset) + outputOffset )
- where y is output and x is the input. The input is the average of any number of sources (through messages) and any number of timesteps. The output goes to any number of targets, again through messages.
-
- It is worth adding that messages can transport arbitrary data structures, so it would also be possible to devise a complicated opaque message directly between solvers. The implementation of Adaptors working on visible fields does this much more transparently and gives the user facile control over the scaling transformation. These adaptors are used especially in the rdesigneur framework of MOOSE, which enables multiscale reaction-diffusion and electrical signaling models.
-
- As an example of this API in operation, I consider two mappings:
- 1. Calcium mapped from electrical to chemical computations.
- 2. phosphorylation state of a channel mapped to the channel conductance.
-
- 1. Calcium mapping.
- Problem statement.
- Calcium is computed in the electrical solver as one or more pools that are fed by calcium currents, and is removed by an exponential decay process. This calcium pool is non-diffusive in the current electrical solver. It has to be mapped to chemical calcium pools at a different spatial discretization, which do diffuse.
-
- In terms of the list of capabilities described above, this is how the API works.
- 1. The electrical model is partitioned into a number of electrical compartments, some of which have the 'electrical' calcium pool as child object in a UNIX filesystem-like tree. Thus the 'electrical' calcium is represented as an object with concentration, location and so on.
- 2. The Solver computes the time-course of evolution of the calcium concentration. Whenever any function queries the 'concentration' field of the calcium object, the Solver provides this value.
- 3. Messaging couples the 'electrical' calcium pool concentration to the adaptor (see point 5). This can either be a 'push' operation, where the solver pushes out the calcium value at its internal update rate, or a 'pull' operation where the adaptor requests the calcium concentration.
- 4. The clock-based scheduler keeps the electrical and chemical solvers ticking away, but it also can drive the operations of the adaptor. Thus the rate of updates to and from the adaptor can be controlled.
- 5. The adaptor averages its inputs. Say the electrical solver is going at a timestep of 50 usec, and the chemical solver at 5000 usec. The adaptor will take 100 samples of the electrical concentration, and average them to compute the 'input' to the linear scaling. Suppose that the electrical model has calcium units of micromolar, but has a zero baseline. The chemical model has units of millimolar and a baseline of 1e-4 millimolar. This gives:
- y = 0.001x + 1e-4
- At the end of this calculation, the adaptor will typically 'push' its output to the chemical solver. Here we have similar situation to item (1), where the chemical entities are calcium pools in space, each with their own calcium concentration. The messaging (3) determines another aspect of the mapping here: the fan in or fan out. In this case, a single electrical compartment may house 10 chemical compartments. Then the output message from the adaptor goes to update the calcium pool concentration on the appropriate 10 objects representing calcium in each of the compartments.
-
- In much the same manner, the phosphorylation state can regulate channel properties.
- 1. The chemical model contains spatially distributed chemical pools that represent the unphosphorylated state of the channel, which in this example is the conducting form. This concentration of this unphosphorylated state is affected by the various reaction- diffusion events handled by the chemical solver, below.
- 2. The chemical solver updates the concentrations of the pool objects as per reaction-diffusion calculations.
- 3. Messaging couples these concentration terms to the adaptor. In this case we have many chemical pool objects for every electrical compartment. There would be a single adaptor for each electrical compartment, and it would average all the input values for calcium concentration, one for each mesh point in the chemical calculation. As before, the access to these fields could be through a 'push' or a 'pull' operation.
- 4. The clock-based scheduler oeperates as above. 5. The adaptor averages the spatially distributed inputs from calcium, and now does a different linear transform. In this case it converts chemical concentration into the channel conductance. As before, the 'electrical' channel is an object (point 1) with a field for conductance, and this term is mapped into the internal data structures of the solver (point 2) invisibly to the user.
-
- .. py:attribute:: proc
-
- void (*shared message field*) This is a shared message to receive Process message from the scheduler.
+This is the adaptor class. It is used in interfacing different kinds of solver
+with each other, especially for electrical to chemical signeur models. The
+Adaptor class is the core of the API for interfacing between different solution
+engines. It is currently in use for interfacing between chemical and electrical
+simulations, but could be used for other cases such as mechanical models. The
+API for interfacing between solution engines rests on the following
+capabilities of MOOSE: 1. The object-oriented interface with classes mapped to
+biological and modeling concepts such as electrical and chemical compartments,
+ion channels and molecular pools. 2. The invisible mapping of Solvers (Objects
+implementing numerical engines) to the object-oriented interface. Solvers work
+behind the scenes to update the objects. 3. The messaging interface which
+allows any visible field to be accessed and updated from any other object. 4.
+The clock-based scheduler which drives operations of any subset of objects at
+any interval. For example, this permits the operations of field access and
+update to take place at quite different timescales from the numerical engines.
+5. The implementation of Adaptor classes. These perform a linear
+ transformation:: (y = scale * (x + inputOffset) + outputOffset ) where y is
+ output and x is the input. The input is the average of any number of sources
+ (through messages) and any number of timesteps. The output goes to any number
+ of targets, again through messages.
+
+It is worth adding that messages can transport arbitrary data structures, so it
+would also be possible to devise a complicated opaque message directly between
+solvers. The implementation of Adaptors working on visible fields does this much
+more transparently and gives the user facile control over the scaling
+transformation. These adaptors are used especially in the rdesigneur framework
+of MOOSE, which enables multiscale reaction-diffusion and electrical signaling
+models.
+
+As an example of this API in operation, I consider two mappings: 1. Calcium
+mapped from electrical to chemical computations. 2. phosphorylation state of a
+channel mapped to the channel conductance.
+
+1. Calcium mapping.
+
+ Problem statement.
+
+ Calcium is computed in the electrical solver as one or more pools that are fed
+ by calcium currents, and is removed by an exponential decay process. This
+ calcium pool is non-diffusive in the current electrical solver. It has to be
+ mapped to chemical calcium pools at a different spatial discretization, which
+ do diffuse.
+
+ In terms of the list of capabilities described above, this is how the API works.
+
+ 1. The electrical model is partitioned into a number of electrical compartments, some of which have the 'electrical' calcium pool as child object in a UNIX filesystem-like tree. Thus the 'electrical' calcium is represented as an object with concentration, location and so on.
+
+ 2. The Solver computes the time-course of evolution of the calcium concentration. Whenever any function queries the 'concentration' field of the calcium object, the Solver provides this value.
+
+ 3. Messaging couples the 'electrical' calcium pool concentration to the adaptor (see point 5). This can either be a 'push' operation, where the solver pushes out the calcium value at its internal update rate, or a 'pull' operation where the adaptor requests the calcium concentration.
+
+ 4. The clock-based scheduler keeps the electrical and chemical solvers ticking away, but it also can drive the operations of the adaptor. Thus the rate of updates to and from the adaptor can be controlled.
+
+ 5. The adaptor averages its inputs. Say the electrical solver is going at a timestep of 50 usec, and the chemical solver at 5000 usec. The adaptor will take 100 samples of the electrical concentration, and average them to compute the 'input' to the linear scaling. Suppose that the electrical model has calcium units of micromolar, but has a zero baseline. The chemical model has units of millimolar and a baseline of 1e-4 millimolar. This gives::
+ y = 0.001x + 1e-4
+
+At the end of this calculation, the adaptor will typically 'push' its
+output to the chemical solver. Here we have similar situation to item (1),
+where the chemical entities are calcium pools in space, each with their
+own calcium concentration. The messaging (3) determines another aspect of
+the mapping here: the fan in or fan out. In this case, a single electrical
+compartment may house 10 chemical compartments. Then the output message
+from the adaptor goes to update the calcium pool concentration on the
+appropriate 10 objects representing calcium in each of the compartments.
+
+In much the same manner, the phosphorylation state can regulate channel
+properties. 1. The chemical model contains spatially distributed chemical pools
+that represent the unphosphorylated state of the channel, which in this
+example is the conducting form. This concentration of this unphosphorylated
+state is affected by the various reaction- diffusion events handled by the
+chemical solver, below. 2. The chemical solver updates the concentrations
+of the pool objects as per reaction-diffusion calculations. 3. Messaging
+couples these concentration terms to the adaptor. In this case we have
+many chemical pool objects for every electrical compartment. There would
+be a single adaptor for each electrical compartment, and it would
+average all the input values for calcium concentration, one for each mesh
+point in the chemical calculation. As before, the access to these fields
+could be through a 'push' or a 'pull' operation. 4. The clock-based
+scheduler oeperates as above. 5. The adaptor averages the spatially distributed
+inputs from calcium, and now does a different linear transform. In this case
+it converts chemical concentration into the channel conductance. As before,
+the 'electrical' channel is an object (point 1) with a field for
+conductance, and this term is mapped into the internal data structures of
+the solver (point 2) invisibly to the user.
+
+.. py:attribute:: proc
+
+ void (*shared message field*) This is a shared message to receive Process message from the scheduler.
.. py:method:: setInputOffset
@@ -343,11 +394,11 @@ Alphabetical listing of moose classes
.. py:class:: CaConc
- CaConc: Calcium concentration pool. Takes current from a channel and keeps track of calcium buildup and depletion by a single exponential process.
+CaConc: Calcium concentration pool. Takes current from a channel and keeps track of calcium buildup and depletion by a single exponential process.
.. py:class:: CaConcBase
- CaConcBase: Base class for Calcium concentration pool. Takes current from a channel and keeps track of calcium buildup and depletion by a single exponential process.
+CaConcBase: Base class for Calcium concentration pool. Takes current from a channel and keeps track of calcium buildup and depletion by a single exponential process.
.. py:attribute:: proc