added few corrected graphs and updated rdes.rst

docs/source/user/py/rdesigneur/rdes.rst

@@ -5,10 +5,13 @@ Author: Upi Bhalla
 
 Date: Aug 26 2016,
 
-Last-Updated: Nov 08 2018
-
+Last-Updated: Oct 28 2020
 By: Upi Bhalla
 
+Git commit  : 65720c1d2e0    (moose-core)
+
+Git commit  : 0ea9dd3c43575e (moose-examples)
+
 .. --------------
 
 Contents
@@ -1004,34 +1007,15 @@ reaction-diffusion system making its way inward from the two ends. After
 the simulation ends the plots for all compartments for the whole run
 come up.
 
-.. figure:: ../../../../images/rdes5_reacdiff.png
-   :alt: Display for oscillatory reaction-diffusion simulation
-
-   Display for oscillatory reaction-diffusion simulation
-
-For those who would rather use the much simpler matplotlib 3-D display option,
-this is what the same simulation looks like:
-
 .. figure:: ../../../../images/ex7.0_spatial_chem_osc.png
    :alt: Display for oscillatory reac-diff simulation using matplotlib
 
    Display for oscillatory reac-diff simulation using matplotlib
 
 
-.. _`moogli primer`:
-
-Primer on using the 3-D MOOGLI display
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-There are two variants of the MOOGLI display. The first, named Moogli,
-uses OpenGL and OpenSceneGraph. It is fast to display, slow to load, and
-difficult to compile. It produces much better looking 3-D graphics.
-The second is a fallback interface using mplot3d, which is a library of 
+The interface using mplot3d, which is a library of 
 Matplotlib and so should be generally available. It is slower to display,
-faster to load, but needs no special compilation. It uses stick graphics
-and though it conveys much the same information, isn't as nice to look at
-as the original Moogli. Its controls are more or less the same but less 
-smooth than the original Moogli.
+faster to load, but needs no special compilation.
 
 Here is a short primer on the 3-D display controls.
 
@@ -1118,7 +1102,9 @@ Calcium-induced calcium release
 
 .. _`models of calcium-induced calcium release`:
 
-*ex7.2_CICR.py*
+*ex7.2_CICR.py *
+
+.. Warning ::	With latest moose-core commit `65720c1d2e0eb8` the result from this example is quite differnt as compared to what shown below, this is due to changes in moose-core. We are working towards this. (See the status here: https://github.com/BhallaLab/moose-examples/issues/73)
 
 This is a somewhat more complex reaction-diffusion system, involving calcium
 release from intracellular stores that propagates in a wave of activity along
@@ -1503,13 +1489,13 @@ so it spikes more, so more calcium enters.
         chanDistrib = [
             ['Na', 'soma', 'Gbar', '300' ],
             ['K_DR', 'soma', 'Gbar', '250' ],
-            ['K_A', 'soma', 'Gbar', '200' ],
+            ['K_A', 'soma', 'Gbar', '250' ],
             ['Ca_conc', 'soma', 'tau', '0.0333' ],
             ['Ca', 'soma', 'Gbar', '40' ]
         ],
         adaptorList = [
             [ 'dend/chan', 'conc', 'K_A', 'modulation', 0.0, 70 ],
-            [ 'Ca_conc', 'Ca', 'dend/Ca', 'conc', 0.00008, 2 ]
+            [ 'Ca_conc', 'Ca', 'dend/Ca', 'conc', 0.00008, 0.8 ]
         ],
         # Give a + pulse from 5 to 7s, and a - pulse from 20 to 21.
         stimList = [['soma', '1', '.', 'inject', '((t>5 && t<7) - (t>20 && t<21)) * 1.0e-12' ]],
@@ -1550,11 +1536,11 @@ rather than to directly assign the conductance *'Gbar'*. This is because
 the electrical segment. This makes it difficult to keep track of. *Modulation*
 is a simple multiplier term onto *Gbar*, and is therefore easier to work with.
 
-       ``[ 'Ca_conc', 'Ca', 'dend/Ca', 'conc', 0.00008, 2 ]``:
+       ``[ 'Ca_conc', 'Ca', 'dend/Ca', 'conc', 0.00008, 0.8 ]``:
 
 Use the concentration of *Ca* as computed in the electrical model, to assign
 the concentration of molecule *Ca* on the dendrite compartment. There is a
-basal level of 80 nanomolar, and every unit of electrical *Ca* maps to 2 
+basal level of 80 nanomolar, and every unit of electrical *Ca* maps to 0.8 
 millimolar of chemical *Ca*.
 
 The arguments in the adaptorList are:
@@ -1651,6 +1637,8 @@ Multiscale model of CICR in dendrite triggered by synaptic input
 
 *ex8.1_synTrigCICR.py*
 
+.. Warning ::	With latest moose-core commit `65720c1d2e0eb8` the result from this example is quite differnt as compared to what shown below, this is due to changes in moose-core. We are working towards this. (See the status here: https://github.com/BhallaLab/moose-examples/issues/74)
+
 In this model synaptic input arrives at a dendritic spine, leading to calcium
 influx through the NMDA receptor. An adaptor converts this influx to the 
 concentration of a chemical species, and this then diffuses into the dendrite
@@ -1764,6 +1752,8 @@ Multiscale model spanning PSD, spine head and dendrite
 
 *ex8.2_multiscale_glurR_phosph_3compt.py*
 
+.. Warning ::	With latest moose-core commit `65720c1d2e0eb8` the result from this example is quite differnt as compared to what shown below, this is due to changes in moose-core. We are working towards this. (See the status here: https://github.com/BhallaLab/moose-examples/issues/74)
+
 This is another multiscale model on similar lines to 8.0. It is structurally
 and computationally more complicated, because the action is distributed between
 spines and dendrites, but formally it does the same thing: it turns on and 
@@ -1910,6 +1900,8 @@ Multiscale model in which spine geometry changes due to signaling
 
 *ex8.3_spine_vol_change.py*
 
+.. Warning ::	With latest moose-core commit `65720c1d2e0eb8` getting runtime error with valueFinfo error. We are working towards this. (See the status here: https://github.com/BhallaLab/moose-examples/issues/75)
+
 This model is very similar to 8.2. The main design difference is that 
 *adaptor*, instead of just modulating the gluR conductance, scales the 
 entire spine cross-section area, with all sorts of electrical and chemical