diff --git a/.github/workflows/basic.yml b/.github/workflows/basic.yml
index 558b930330cbe7fa42c5c33d987004c6099535e7..7bf0902c54be06d4bde8180bbb3f31ceb134c949 100644
--- a/.github/workflows/basic.yml
+++ b/.github/workflows/basic.yml
@@ -75,7 +75,7 @@ jobs:
mkdir build
cd build
cmake .. -DCMAKE_CXX_COMPILER=$CXX -DCMAKE_C_COMPILER=$CC -DARB_WITH_PYTHON=ON -DPYTHON_EXECUTABLE=`which python` -DARB_WITH_MPI=${{ matrix.mpi }}
- make -j4 tests examples html pyarb
+ make -j4 tests examples pyarb html
cd -
- name: Run unit tests
run: build/bin/unit
@@ -93,3 +93,5 @@ jobs:
python python/example/single_cell_model.py
python python/example/single_cell_recipe.py
python python/example/single_cell_swc.py test/unit/swc/pyramidal.swc
+ python python/example/single_cell_detailed.py python/example/morph.swc
+ python python/example/single_cell_detailed_recipe.py python/example/morph.swc
diff --git a/doc/CMakeLists.txt b/doc/CMakeLists.txt
index 1b3f23f5d305268b8060193a8c7d3a9c5b9a89f9..5889d51a7252c576f888986ded087a7679efa678 100644
--- a/doc/CMakeLists.txt
+++ b/doc/CMakeLists.txt
@@ -20,7 +20,9 @@ string(REPLACE ";" "," theme_path "${theme_path}")
add_custom_target(html
COMMAND
- ${SPHINX_EXECUTABLE}
+ PYTHONPATH=${CMAKE_BINARY_DIR}/python
+ ${PYTHON_EXECUTABLE}
+ -m sphinx
-b html
-d ${doctree_dir}
-D "html_theme_path=${theme_path}"
diff --git a/doc/concepts/recipe.rst b/doc/concepts/recipe.rst
index 8223f321e3ee2987a23f8d9cab79855a9252222d..35ee8333a999f60c7d494babe0e86b44009749e4 100644
--- a/doc/concepts/recipe.rst
+++ b/doc/concepts/recipe.rst
@@ -4,18 +4,18 @@ Recipes
=======
An Arbor *recipe* is a description of a model. The recipe is queried during the model
-building phase to provide information about cells in the model, such as:
+building phase to provide information about individual cells in the model, such as:
* The **number of cells** in the model.
* The **kind** of each cell.
* The **description** of each cell, e.g. with morphology, dynamics, synapses, detectors,
stimuli etc.
- * The number of **spike targets**.
- * The number of **spike sources**.
- * The number of **gap junction sites**.
+ * The number of **spike targets** on each cell.
+ * The number of **spike sources** on each cell.
+ * The number of **gap junction sites** on each cell.
* Incoming **network connections** from other cells terminating on a cell.
- * **Gap junction connections** on a cell.
- * **Probes** on a cell.
+ * **Gap junction connections** on each cell.
+ * **Probes** on each cell.
To better illustrate the content of a recipe, let's consider the following network of
three cells:
diff --git a/doc/contrib/coding-style.rst b/doc/contrib/coding-style.rst
index cd5b79a8905651450a524fc6be47be0bd5803c2f..21dd9c6d20c64dbcec97b058b94dc29d03696444 100644
--- a/doc/contrib/coding-style.rst
+++ b/doc/contrib/coding-style.rst
@@ -1,4 +1,4 @@
-.. _contribcodingstyle
+.. _contribcodingstyle:
Coding Guidelines
=================
diff --git a/doc/index.rst b/doc/index.rst
index 746b51dfab58a4466143d7e6d6cb2e42f6ed923c..0424bb7b259f6b34db56e1313e81fabd9e0d03c7 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -6,7 +6,7 @@ Arbor
Welcome to the documentation for Arbor, the multi-compartment neural network simulation library.
-You can find out how to :ref:`get Arbor<in_install>`; get started quickly with our :ref:`tutorials<gs_other_examples>`; or continue reading to learn more about Arbor.
+You can find out how to :ref:`get Arbor<in_install>`; get started quickly with our :ref:`tutorials<tutorial>`; or continue reading to learn more about Arbor.
What is Arbor?
--------------
@@ -23,7 +23,7 @@ Arbor is open source and openly developed, and we use development practices such
Documentation organisation
--------------------------
-* :ref:`gs_other_examples` contains a few ready-made examples you can use to quickly get started using Arbor. In the tutorial descriptions we link to the relevant Arbor concepts.
+* :ref:`tutorial` contains a few ready-made examples you can use to quickly get started using Arbor. In the tutorial descriptions we link to the relevant Arbor concepts.
* :ref:`modelintro` describes the design and concepts used in Arbor. The breakdown of concepts is mirrored (as much as possible) in the :ref:`pyoverview` and :ref:`cppoverview`, so you can easily switch between languages and concepts.
* :ref:`hpc-overview` details Arbor-features for distribution of computation over supercomputer nodes.
* :ref:`internals-overview` describes Arbor code that is not user-facing; convenience classes, architecture abstractions, etc.
diff --git a/doc/install/python.rst b/doc/install/python.rst
index 2ed52291b61d80e1c8c70338ec6c8f9a23f4081d..d515bf12df85843d097b9b9f47b95fcb61a9f6a8 100644
--- a/doc/install/python.rst
+++ b/doc/install/python.rst
@@ -41,7 +41,7 @@ to see information about the version and enabled features:
>>> print(arbor.__config__)
You are now ready to use Arbor! You can continue reading these documentation pages, have a look at the
-:ref:`Python API reference<pyoverview>`, or visit the :ref:`gs_other_examples`.
+:ref:`Python API reference<pyoverview>` , or visit the :ref:`Quick Start page<tutorialsimplecell>`.
.. Note::
To get help in case of problems installing with pip, run pip with the ``--verbose`` flag, and attach the output
diff --git a/doc/python/cable_cell.rst b/doc/python/cable_cell.rst
index 03affce555da07ec6f7c1228d9fd1e04b76f0128..c0e2a7f7902b61fc5c8823cb2ada7784286c2916 100644
--- a/doc/python/cable_cell.rst
+++ b/doc/python/cable_cell.rst
@@ -207,7 +207,7 @@ Cable cells
import arbor
# Construct the morphology from an SWC file.
- tree = arbor.load_swc('granule.swc')
+ tree = arbor.load_swc_arbor('granule.swc')
morph = arbor.morphology(tree)
# Define regions using standard SWC tags
diff --git a/doc/python/morphology.rst b/doc/python/morphology.rst
index c4e20bc1d381443f25ac6b203f25bf1e54a9e9b5..1dc0d9136c10bb27b3d04924b0c700f4bbf2f4b6 100644
--- a/doc/python/morphology.rst
+++ b/doc/python/morphology.rst
@@ -317,7 +317,7 @@ Cell morphology
:param int i: branch index
:rtype: list
-.. py:function:: load_swc(filename)
+.. py:function:: load_swc_arbor(filename)
Loads the :class:`morphology` from an SWC file according to arbor's SWC specifications.
(See the morphology concepts :ref:`page <morph-formats>` for more details).
diff --git a/doc/scripts/gen-labels.py b/doc/scripts/gen-labels.py
index 1f5b5c6df914dc66ad53ddeb96f377cc4d850888..22a5dd0aa660a17b66769c12fe0f1ba127286b49 100644
--- a/doc/scripts/gen-labels.py
+++ b/doc/scripts/gen-labels.py
@@ -159,7 +159,7 @@ tree.append(8, mpoint(-14.5,-0.1, 0.0, 0.5), tag=2)
ysoma_morph3 = arbor.morphology(tree)
fn = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__), "../concepts/example.swc"))
-swc_morph = arbor.load_swc(fn)
+swc_morph = arbor.load_swc_arbor(fn)
regions = {
'empty': '(nil)',
@@ -214,8 +214,49 @@ d = arbor.label_dict(labels)
# Create a cell to concretise the region and locset definitions
cell = arbor.cable_cell(label_morph, d, arbor.decor())
+###############################################################################
+# Tutorial Example
+###############################################################################
+
+tree = arbor.segment_tree()
+tree.append(mnpos, mpoint(0, 0.0, 0, 2.0), mpoint( 4, 0.0, 0, 2.0), tag=1)
+tree.append(0, mpoint(4, 0.0, 0, 0.8), mpoint( 8, 0.0, 0, 0.8), tag=3)
+tree.append(1, mpoint(8, 0.0, 0, 0.8), mpoint(12, -0.5, 0, 0.8), tag=3)
+tree.append(2, mpoint(12, -0.5, 0, 0.8), mpoint(20, 4.0, 0, 0.4), tag=3)
+tree.append(3, mpoint(20, 4.0, 0, 0.4), mpoint(26, 6.0, 0, 0.2), tag=3)
+tree.append(2, mpoint(12, -0.5, 0, 0.5), mpoint(19, -3.0, 0, 0.5), tag=3)
+tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(24, -7.0, 0, 0.2), tag=4)
+tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(23, -1.0, 0, 0.2), tag=4)
+tree.append(7, mpoint(23, -1.0, 0, 0.2), mpoint(36, -2.0, 0, 0.2), tag=4)
+tree.append(mnpos, mpoint(0, 0.0, 0, 2.0), mpoint(-7, 0.0, 0, 0.4), tag=2)
+tree.append(9, mpoint(-7, 0.0, 0, 0.4), mpoint(-10, 0.0, 0, 0.4), tag=2)
+tutorial_morph = arbor.morphology(tree)
+
+tutorial_regions = {
+ 'all': '(all)',
+ 'soma': '(tag 1)',
+ 'axon': '(tag 2)',
+ 'dend': '(tag 3)',
+ 'last': '(tag 4)',
+ 'rad_gt': '(radius-ge (region "all") 1.5)',
+ 'custom': '(join (region "last") (region "rad_gt"))'
+}
+tutorial_locsets = {
+ 'root': '(root)',
+ 'terminal': '(terminal)',
+ 'custom_terminal': '(restrict (locset "terminal") (region "custom"))',
+ 'axon_terminal': '(restrict (locset "terminal") (region "axon"))'
+
+}
+
+tutorial_labels = {**tutorial_regions, **tutorial_locsets}
+tutorial_dict = arbor.label_dict(tutorial_labels)
+
+# Create a cell to concretise the region and locset definitions
+tutorial_cell = arbor.cable_cell(tutorial_morph, tutorial_dict, arbor.decor())
+
################################################################################
-# Output all of the morphologies and reion/locset definitions to a Python script
+# Output all of the morphologies and region/locset definitions to a Python script
# that can be run during the documentation build to generate images.
################################################################################
f = open(sys.argv[1] + '/inputs.py', 'w')
@@ -235,16 +276,27 @@ f.write(write_morphology('yshaped_morph', yshaped_morph))
f.write(write_morphology('ysoma_morph1', ysoma_morph1))
f.write(write_morphology('ysoma_morph2', ysoma_morph2))
f.write(write_morphology('ysoma_morph3', ysoma_morph3))
+f.write(write_morphology('tutorial_morph', tutorial_morph))
f.write(write_morphology('swc_morph', swc_morph))
-f.write('\n############# locsets\n\n')
+f.write('\n############# locsets (label_morph)\n\n')
for label in locsets:
locs = [(l.branch, l.pos) for l in cell.locations('"{}"'.format(label))]
f.write('ls_{} = {{\'type\': \'locset\', \'value\': {}}}\n'.format(label, locs))
-f.write('\n############# regions\n\n')
+f.write('\n############# regions (label_morph)\n\n')
for label in regions:
comps = [(c.branch, c.prox, c.dist) for c in cell.cables('"{}"'.format(label))]
f.write('reg_{} = {{\'type\': \'region\', \'value\': {}}}\n'.format(label, comps))
+f.write('\n############# locsets (tutorial_morph)\n\n')
+for label in tutorial_locsets:
+ locs = [(l.branch, l.pos) for l in tutorial_cell.locations('"{}"'.format(label))]
+ f.write('tut_ls_{} = {{\'type\': \'locset\', \'value\': {}}}\n'.format(label, locs))
+
+f.write('\n############# regions (tutorial_morph)\n\n')
+for label in tutorial_regions:
+ comps = [(c.branch, c.prox, c.dist) for c in tutorial_cell.cables('"{}"'.format(label))]
+ f.write('tut_reg_{} = {{\'type\': \'region\', \'value\': {}}}\n'.format(label, comps))
+
f.close()
diff --git a/doc/scripts/make_images.py b/doc/scripts/make_images.py
index da9f9d8936cf42994e2b98b5998e5bea2fc77e9e..69edbde370e2e8c4ade68bae64541505e50fea5a 100644
--- a/doc/scripts/make_images.py
+++ b/doc/scripts/make_images.py
@@ -3,7 +3,7 @@ import svgwrite
import math
import inputs
-tag_colors = ['white', '#ffc2c2', 'gray', '#c2caff']
+tag_colors = ['white', '#ffc2c2', 'gray', '#c2caff', '#81c8aa']
#
# ############################################
@@ -282,6 +282,20 @@ def generate(path=''):
label_image(inputs.label_morph, [inputs.reg_radgt5], path+'/radiusgt_label.svg')
label_image(inputs.label_morph, [inputs.reg_radge5], path+'/radiusge_label.svg')
+ ####################### Tutorial examples
+
+ morph_image([inputs.tutorial_morph], ['segments'], path+'/tutorial_morph.svg')
+
+ ####################### locsets
+
+ label_image(inputs.tutorial_morph, [inputs.tut_ls_root, inputs.tut_ls_terminal], path+'/tutorial_root_term.svg')
+ label_image(inputs.tutorial_morph, [inputs.tut_ls_custom_terminal, inputs.tut_ls_axon_terminal], path+'/tutorial_custom_axon_term.svg')
+
+
+ ####################### regions
+ label_image(inputs.tutorial_morph, [inputs.tut_reg_soma, inputs.tut_reg_axon, inputs.tut_reg_dend, inputs.tut_reg_last], path+'/tutorial_tag.svg')
+ label_image(inputs.tutorial_morph, [inputs.tut_reg_all, inputs.tut_reg_rad_gt], path+'/tutorial_all_gt.svg')
+ label_image(inputs.tutorial_morph, [inputs.tut_reg_custom], path+'/tutorial_custom.svg')
if __name__ == '__main__':
generate('.')
diff --git a/doc/tutorial/index.rst b/doc/tutorial/index.rst
index 77d30e81944219605f8a3fc5122fdb18b75e12dd..f52907ddf6a2241b91be1c2543b942854ac92bff 100644
--- a/doc/tutorial/index.rst
+++ b/doc/tutorial/index.rst
@@ -1,14 +1,25 @@
-.. _gs_other_examples:
+.. _tutorial:
Tutorials
=========
.. Note::
- You can find some examples of full Arbor simulations in the ``python/examples`` directory of the `Arbor repository <https://github.com/arbor-sim/arbor>`_.
- Note that some examples use ``pandas`` and ``seaborn`` for analysis and plotting which are expected to be installed independently from Arbor.
+ You can find some examples of full Arbor simulations in the ``python/examples`` directory of the
+ `Arbor repository <https://github.com/arbor-sim/arbor>`_.
+
+ The examples use ``pandas`` and ``seaborn`` for analysis and plotting which are expected to be
+ installed independently from Arbor.
+
+ In an interactive Python interpreter, you can use ``help()`` on any class or function to get its
+ documentation. (Try, ``help(arbor.simulation``, for example).
+
+.. Todo::
+ Add more in-depth tutorial-like pages building up examples here.
.. toctree::
:maxdepth: 1
:caption: Tutorials
single_cell_model
+ single_cell_detailed
+ single_cell_detailed_recipe
diff --git a/doc/tutorial/single_cell_detailed.rst b/doc/tutorial/single_cell_detailed.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6558a5b0e6f9807bbac1d414c85b7bbda8fd1cd6
--- /dev/null
+++ b/doc/tutorial/single_cell_detailed.rst
@@ -0,0 +1,505 @@
+.. _tutorialsinglecellswc:
+
+A detailed single cell model
+============================
+
+We can expand on the :ref:`single segment cell example <tutorialsimplecell>` to create a more
+complex single cell model, and go through the process in more detail.
+
+.. Note::
+
+ **Concepts covered in this example:**
+
+ 1. Building a morphology from a :class:`arbor.segment_tree`.
+ 2. Building a morphology from an SWC file.
+ 3. Writing and visualizing region and locset expressions.
+ 4. Building a decor.
+ 5. Discretising the morphology.
+ 6. Setting and overriding model and cell parameters.
+ 7. Running a simulation and visualising the results using a :class:`arbor.single_cell_model`.
+
+We start by building the cell. This will be a :ref:`cable cell <cablecell>` with complex
+geometry and dynamics which can be constructed from 3 components:
+
+1. A **morphology** defining the geometry of the cell.
+2. A **label dictionary** storing labeled expressions which define regions and locations of
+ interest on the cell.
+3. A **decor** defining various properties and dynamics on these labeled regions and locations.
+ The decor also includes hints about how the cell is to be modeled under the hood, by
+ splitting it into discrete control volumes (CV).
+
+Next, we construct a :class:`arbor.single_cell_model`. This model takes care of a lot of details
+behind the scenes: it sets up a recipe (more on recipes :ref:`here <modelrecipe>`), creates
+a simulation object, manages the hardware etc. These details become more important when modeling
+a network of cells, but can be abstracted away when working with single cell networks.
+
+The single cell model has 4 main functions:
+
+1. It holds the **global properties** of the model
+2. It registers **probes** on specific locations on the cell to measure the voltage.
+3. It **runs** the simulation.
+4. It collects **spikes** from spike detectors and voltage **traces** from registered probes.
+
+.. _tutorialsinglecellswc-cell:
+
+The cell
+********
+
+Before creating the actual cell object, we have to create its components.
+
+The morpholohgy
+^^^^^^^^^^^^^^^
+We begin by constructing the following morphology:
+
+.. figure:: ../gen-images/tutorial_morph.svg
+ :width: 400
+ :align: center
+
+This can be done by manually building a segment tree:
+
+.. code-block:: python
+
+ import arbor
+ from arbor import mpoint
+ from arbor import mnpos
+
+ # Define the morphology by manually building a segment tree
+
+ tree = arbor.segment_tree()
+
+ # Start with segment 0: a cylindrical soma with tag 1
+ tree.append(mnpos, mpoint(0.0, 0.0, 0.0, 2.0), mpoint( 40.0, 0.0, 0.0, 2.0), tag=1)
+ # Construct the first section of the dendritic tree with tag 3,
+ # comprised of segments 1 and 2, attached to soma segment 0.
+ tree.append(0, mpoint(40.0, 0.0, 0.0, 0.8), mpoint( 80.0, 0.0, 0.0, 0.8), tag=3)
+ tree.append(1, mpoint(80.0, 0.0, 0.0, 0.8), mpoint(120.0, -5.0, 0.0, 0.8), tag=3)
+ # Construct the rest of the dendritic tree: segments 3, 4 and 5.
+ tree.append(2, mpoint(120.0, -5.0, 0.0, 0.8), mpoint(200.0, 40.0, 0.0, 0.4), tag=3)
+ tree.append(3, mpoint(200.0, 40.0, 0.0, 0.4), mpoint(260.0, 60.0, 0.0, 0.2), tag=3)
+ tree.append(2, mpoint(120.0, -5.0, 0.0, 0.5), mpoint(190.0, -30.0, 0.0, 0.5), tag=3)
+ # Construct a special region of the tree made of segments 6, 7, and 8
+ # differentiated from the rest of the tree using tag 4.
+ tree.append(5, mpoint(190.0, -30.0, 0.0, 0.5), mpoint(240.0, -70.0, 0.0, 0.2), tag=4)
+ tree.append(5, mpoint(190.0, -30.0, 0.0, 0.5), mpoint(230.0, -10.0, 0.0, 0.2), tag=4)
+ tree.append(7, mpoint(230.0, -10.0, 0.0, 0.2), mpoint(360.0, -20.0, 0.0, 0.2), tag=4)
+ # Construct segments 9 and 10 that make up the axon with tag 2.
+ # Segment 9 is at the root, where its proximal end will be connected to the
+ # proximal end of the soma segment.
+ tree.append(mnpos, mpoint( 0.0, 0.0, 0.0, 2.0), mpoint( -70.0, 0.0, 0.0, 0.4), tag=2)
+ tree.append(9, mpoint(-70.0, 0.0, 0.0, 0.4), mpoint(-100.0, 0.0, 0.0, 0.4), tag=2)
+
+ morph = arbor.morphology(tree);
+
+The same morphology can be represented using an SWC file (interpreted according
+to :ref:`Arbor's specifications <morph-formats>`). We can save the following in
+``morph.swc``.
+
+.. code-block:: python
+
+ # id, tag, x, y, z, r, parent
+ 1 1 0.0 0.0 0.0 2.0 -1 # seg0 prox / seg9 prox
+ 2 1 40.0 0.0 0.0 2.0 1 # seg0 dist
+ 3 3 40.0 0.0 0.0 0.8 2 # seg1 prox
+ 4 3 80.0 0.0 0.0 0.8 3 # seg1 dist / seg2 prox
+ 5 3 120.0 -5.0 0.0 0.8 4 # seg2 dist / seg3 prox
+ 6 3 200.0 40.0 0.0 0.4 5 # seg3 dist / seg4 prox
+ 7 3 260.0 60.0 0.0 0.2 6 # seg4 dist
+ 8 3 120.0 -5.0 0.0 0.5 5 # seg5 prox
+ 9 3 190.0 -30.0 0.0 0.5 8 # seg5 dist / seg6 prox / seg7 prox
+ 10 4 240.0 -70.0 0.0 0.2 9 # seg6 dist
+ 11 4 230.0 -10.0 0.0 0.2 9 # seg7 dist / seg8 prox
+ 12 4 360.0 -20.0 0.0 0.2 11 # seg8 dist
+ 13 2 -70.0 0.0 0.0 0.4 1 # seg9 dist / seg10 prox
+ 14 2 -100.0 0.0 0.0 0.4 13 # seg10 dist
+
+.. Note::
+
+ SWC samples always form a segment with their parent sample. For example,
+ sample 3 and sample 2 form a segment which has length = 0.
+ We use these zero-length segments to represent an abrupt radius change
+ in the morphology, like we see between segment 0 and segment 1 in the above
+ morphology diagram.
+
+ More information on SWC loaders can be found :ref:`here <morph-formats>`.
+
+The morphology can then be loaded from ``morph.swc`` in the following way:
+
+.. code-block:: python
+
+ import arbor
+
+ # Read the morphology from an SWC file
+
+ morph = arbor.load_swc_arbor("morph.swc")
+
+The label dictionary
+^^^^^^^^^^^^^^^^^^^^
+
+Next, we can define **region** and **location** expressions and give them labels.
+The regions and locations are defined using an Arbor-specific DSL, and the labels
+can be stored in a :class:`arbor.lable_dict`.
+
+.. Note::
+
+ The expressions in the label dictionary don't actually refer to any concrete regions
+ or locations of the morphology at this point. They are merely descriptions that can be
+ applied to any morphology, and depending on its geometry, they will generate different
+ regions and locations. However, we will show some figures illustrating the effect of
+ applying these expressions to the above morphology, in order to better visualize the
+ final cell.
+
+ More information on region and location expressions is available :ref:`here <labels>`.
+
+First, we can define some **regions**, These are continuous parts of the morphology,
+They can correspond to full segments or parts of segments. Our morphology already has some
+pre-established regions determined by the ``tag`` parameter of the segments. They are
+defined as follows:
+
+.. code-block:: python
+
+ #Create a label dictionary
+
+ labels = arbor.label_dict()
+
+ # Add labels for tag 1, 2, 3, 4
+ labels['soma'] = '(tag 1)'
+ labels['axon'] = '(tag 2)'
+ labels['dend'] = '(tag 3)'
+ labels['last'] = '(tag 4)'
+
+This will generate the following regions when applied to the previously defined morphology:
+
+.. figure:: ../gen-images/tutorial_tag.svg
+ :width: 800
+ :align: center
+
+ From left to right: regions "soma", "axon", "dend" and "last"
+
+We can also define a region that represents the whole cell; and to make things a bit more interesting,
+a region that includes the parts of the morphology that have a radius greater than 1.5 μm. This is done
+in the following way:
+
+.. code-block:: python
+
+ # Add a label for a region that includes the whole morphology
+ labels['all'] = '(all)'
+
+ # Add a label for the parts of the morphology with radius greater than 1.5 μm.
+ labels['gt_1.5'] = '(radius-gt (region "all") 1.5)'
+
+This will generate the following regions when applied to the previously defined morphology:
+
+.. figure:: ../gen-images/tutorial_all_gt.svg
+ :width: 400
+ :align: center
+
+ Left: region "all"; right: region "gt_1.5"
+
+By comparing to the original morphology, we can see region "gt_1.5" includes all of segment 0 and part of
+segment 9.
+
+Finally, let's define a region that includes two already defined regions: "last" and "gt_1.5". This can
+be done as follows:
+
+.. code-block:: python
+
+ # Join regions "last" and "gt_1.5"
+ labels['custom'] = '(join (region "last") (region "gt_1.5"))'
+
+This will generate the following region when applied to the previously defined morphology:
+
+.. figure:: ../gen-images/tutorial_custom.svg
+ :width: 200
+ :align: center
+
+Our label dictionary so far only contains regions. We can also add some **locations**. Let's start
+with a location that is the root of the morphology, and the set of locations that represent all the
+terminal points of the morphology.
+
+.. code-block:: python
+
+ # Add a labels for the root of the morphology and all the terminal points
+ labels['root'] = '(root)'
+ labels['terminal'] = '(terminal)'
+
+This will generate the following **locsets** (sets of one or more locations) when applied to the
+previously defined morphology:
+
+.. figure:: ../gen-images/tutorial_root_term.svg
+ :width: 400
+ :align: center
+
+ Left: locset "root"; right: locset "terminal"
+
+To make things more interesting, let's select only the terminal points which belong to the
+previously defined "custom" region; and, separately, the terminal points which belong to the
+"axon" region:
+
+.. code-block:: python
+
+ # Add a label for the terminal locations in the "custom" region:
+ labels['custom_terminal'] = '(restrict (locset "terminal") (region "custom"))'
+
+ # Add a label for the terminal locations in the "axon" region:
+ labels['axon_terminal'] = '(restrict (locset "terminal") (region "axon"))'
+
+This will generate the following 2 locsets when applied to the previously defined morphology:
+
+.. figure:: ../gen-images/tutorial_custom_axon_term.svg
+ :width: 400
+ :align: center
+
+ Left: locset "custom_terminal"; right: locset "axon_terminal"
+
+The decorations
+^^^^^^^^^^^^^^^
+
+With the key regions and location expressions identified and labeled, we can start to
+define certain features, properties and dynamics on the cell. This is done through a
+:class:`arbor.decor` object, which stores a mapping of these "decorations" to certain
+region or location expressions.
+
+.. Note::
+
+ Similar to the label dictionary, the decor object is merely a description of how an abstract
+ cell should behave, which can then be applied to any morphology, and have a different effect
+ depending on the geometry and region/locset expressions.
+
+ More information on decors can be found :ref:`here <cablecell-decoration>`.
+
+The decor object can have default values for properties, which can then be overridden on specific
+regions. It is in general better to explicitly set all the default properties of your cell,
+to avoid the confusion to having simulator-specific default values. This will therefore be our first
+step:
+
+.. code-block:: python
+
+ # Create a decor object
+ decor = arbor.decor()
+
+ # Set the default properties
+ decor.set_property(Vm =-55, tempK=300, rL=35.4, cm=0.01)
+ decor.set_ion('na', int_con=10, ext_con=140, rev_pot=50, method='nernst/na')
+ decor.set_ion('k', int_con=54.4, ext_con=2.5, rev_pot=-77)
+
+We have set the default initial membrane voltage to -55 mV; the default initial
+temperature to 300 K; the default axial resistivity to 35.4 Ω·cm; and the default membrane
+capacitance to 0.01 F/m².
+
+We also set the initial properties of the *na* and *k* ions because they will be utilized
+by the density mechanisms that we will be adding shortly.
+For both ions we set the default initial concentration and external concentration measures in mM;
+and we set the default initial reversal potential in mV. For the *na* ion, we additionally indicate
+the the progression on the reversal potential during the simulation will be dictated by the
+`nernst equation <https://en.wikipedia.org/wiki/Nernst_equation>`_.
+
+It happens, however, that we want the temperature of the "custom" region defined in the label
+dictionary earlier to be colder, and the initial voltage of the "soma" region to be higher.
+We can override the default properties by *painting* new values on the relevant regions using
+:meth:`arbor.decor.paint`.
+
+.. code-block:: python
+
+ # Override default parameters on certain regions
+
+ decor.paint('"custom"', tempK=270)
+ decor.paint('"soma"', Vm=-50)
+
+With the default and initial values taken care of, we now add some density mechanisms. Let's *paint*
+a *pas* mechanism everywhere on the cell using the previously defined "all" region; an *hh* mechanism
+on the "custom" region; and an *Ih* mechanism on the "dend" region. The *Ih* mechanism is explicitly
+constructed in order to change the default values of its 'gbar' parameter.
+
+
+.. code-block:: python
+
+ # Paint density mechanisms on certain regions
+
+ from arbor import mechanism as mech
+
+ decor.paint('"all"', 'pas')
+ decor.paint('"custom"', 'hh')
+ decor.paint('"dend"', mech('Ih', {'gbar': 0.001}))
+
+The decor object is also used to *place* stimuli and spike detectors on the cell using :meth:`arbor.decor.place`.
+We place 3 current clamps of 2 nA on the "root" locset defined earlier, starting at time = 10, 30, 50 ms and
+lasting 1ms each. As well as spike detectors on the "axon_terminal" locset for voltages above -10 mV:
+
+.. code-block:: python
+
+ # Place stimuli and spike detectors on certain locsets
+
+ decor.place('"root"', arbor.iclamp(10, 1, current=2))
+ decor.place('"root"', arbor.iclamp(30, 1, current=2))
+ decor.place('"root"', arbor.iclamp(50, 1, current=2))
+ decor.place('"axon_terminal"', arbor.spike_detector(-10))
+
+Finally, there's one last property that impacts the behavior of a model: the discretisation.
+Cells in Arbor are simulated as discrete components called control volumes (CV). The size of
+a CV has an impact on the accuracy of the results of the simulation. Usually, smaller CVs
+are more accurate because they simulate the continuous nature of a neuron more closely.
+
+The user controls the discretisation using a :class:`arbor.cv_policy`. There are a few different policies to
+choose from, and they can be composed with one another. In this example, we would like the "soma" region
+to be a single CV, and the rest of the morphology to be comprised of CVs with a maximum length of 1 μm:
+
+.. code-block:: python
+
+ # Single CV for the "soma" region
+ soma_policy = arbor.cv_policy_single('"soma"')
+
+ # CVs with max length = 1 μm as default
+ dflt_policy = arbor.cv_policy_max_extent(1.0)
+
+ # default policy everywhere except the soma
+ policy = dflt_policy | soma_policy
+
+ decor.discretization(policy)
+
+
+The model
+*********
+
+We begin by constructing a :class:`arbor.single_cell_model` of the cell we just created.
+
+.. code-block:: python
+
+ # Construct the model
+
+ model = arbor.single_cell_model(cell)
+
+The global properties
+^^^^^^^^^^^^^^^^^^^^^
+
+The global properties of a single cell model include:
+
+1. The **mechanism catalogue**: A mechanism catalogue is a collection of density and point
+ mechanisms. Arbor has 3 built in mechanism catalogues: default, allen and bbp. The mechanism
+ catalogue in the global properties of the model must include the catalogues of all the
+ mechanisms painted on the cell decor.
+
+2. The default **parameters**: The initial membrane voltage; the initial temperature; the
+ axial resistivity; the membrane capacitance; the ion parameters; and the discretisation
+ policy.
+
+.. Note::
+
+ You may notice that the same parameters can be set both at the cell level and at
+ the model level. This is intentional. The model parameters apply to all the cells in a model,
+ whereas the cell parameters apply only to that specific cell.
+
+ The idea is that the user is able to define a set of global properties for all cells in a model
+ which can then be overridden for individual cells, and overridden yet again on certain
+ regions of the cells.
+
+ You may now be wondering why this is needed for the `single cell model` where there is only one
+ cell by design. You can use this feature to ease moving from simulating a set of single cell models
+ to simulating a network of these cells.
+ For example, a user may choose to individually test several single cell models before simulating
+ their interactions. By using the same global properties for each *model*, and customizing the *cell*
+ global properties, it becomes possible to use the cell descriptions of each cell, unchanged, in a
+ larger network model.
+
+Earlier in the example we mentioned that it is better to explicitly set all the default properties
+of your cell, while that is true, it is better yet to set the default properties of the entire
+model:
+
+.. _tutorialsinglecellswc-gprop:
+
+.. code-block:: python
+
+ # Set the model default properties
+
+ model.properties.set_property(Vm =-65, tempK=300, rL=35.4, cm=0.01)
+ model.properties.set_ion('na', int_con=10, ext_con=140, rev_pot=50, method='nernst/na')
+ model.properties.set_ion('k', int_con=54.4, ext_con=2.5, rev_pot=-77)
+
+We set the same properties as we did earlier when we were creating the *decor* of the cell, except
+for the initial membrane voltage, which is -65 mV as opposed to -55 mV.
+
+During the decoration step, we also made use of 3 mechanisms: *pas*, *hh* and *Ih*. As it happens,
+the *pas* and *hh* mechanisms are in the default Arbor catalogue, whereas the *Ih* mechanism is in
+the "allen" catalogue. We can extend the default catalogue as follow:
+
+.. code-block:: python
+
+ # Extend the default catalogue with the allen catalogue.
+ # The function takes a second string parameter that can prefix
+ # the name of the mechanisms to avoid collisions between catalogues
+ # in this case we have no collisions so we use an empty prefix string.
+
+ model.catalogue.extend(arbor.allen_catalogue(), "")
+
+Now all three mechanisms in the *decor* object have been made available to the model.
+
+The probes
+^^^^^^^^^^
+
+The model is almost ready for simulation. Except that the only output we would be able to
+measure at this point is the spikes from the spike detectors placed in the decor.
+
+The :class:`arbor.single_cell_model` can also measure the voltage on specific locations of the cell.
+We can indicate the location we would like to probe using labels from the :class:`label_dict`:
+
+.. code-block:: python
+
+ # Add voltage probes on the "custom_terminal" locset
+ # which sample the voltage at 50000 Hz
+
+ model.probe('voltage', where='"custom_terminal"', frequency=50000)
+
+The simulation
+^^^^^^^^^^^^^^
+
+The cell and model descriptions are now complete and we can run the simulation:
+
+.. code-block:: python
+
+ # Run the simulation for 100 ms, with a dt of 0.025 ms
+ model.run(tfinal=100, dt=0.025)
+
+The results
+^^^^^^^^^^^
+
+Finally we move on to the data collection segment of the example. We have added a spike detector
+on the "axon_terminal" locset. The :class:`arbor.single_cell_model` automatically registers all
+spikes on the cell from all spike detectors on the cell and saves the times at which they occurred.
+
+.. code-block:: python
+
+ # Print the number of spikes.
+ print(len(model.spikes), 'spikes recorded:')
+
+ # Print the spike times.
+ for s in model.spikes:
+ print(s)
+
+
+A more interesting result of the simulation is perhaps the output of the voltage probe previously
+placed on the "custom_terminal" locset. The model saves the output of the probes as [time, value]
+pairs which can then be plotted. We use `pandas` and `seaborn` for the plotting, but the user can
+choose the any other library:
+
+.. code-block:: python
+
+ import pandas
+ import seaborn
+
+ # Plot the output of the probes
+ df = pandas.DataFrame()
+ for t in model.traces:
+ df=df.append(pandas.DataFrame({'t/ms': t.time, 'U/mV': t.value, 'Location': str(t.location), 'Variable': t.variable}))
+
+ seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable",ci=None).savefig('single_cell_detailed_result.svg')
+
+The following plot is generated. The orange line is slightly delayed from the blue line, which is
+what we'd expect because branch 4 is longer than branch 3 of the morphology. We also see 3 spikes,
+corresponding to each of the current clamps placed on the cell.
+
+.. figure:: single_cell_detailed_result.svg
+ :width: 400
+ :align: center
+
+The full code
+*************
+You can find the full code of the example at ``python/examples/single_cell_detailed.py``.
\ No newline at end of file
diff --git a/doc/tutorial/single_cell_detailed_recipe.rst b/doc/tutorial/single_cell_detailed_recipe.rst
new file mode 100644
index 0000000000000000000000000000000000000000..32df752764c9c91f4d3b8649a1718ee84a06e784
--- /dev/null
+++ b/doc/tutorial/single_cell_detailed_recipe.rst
@@ -0,0 +1,419 @@
+.. _tutorialsinglecellrecipe:
+
+A detailed single cell recipe
+=============================
+
+This example aims to build the same single cell model as
+:ref:`the previous tutorial <tutorialsinglecellswc>`, except using a :class:`arbor.recipe`
+and :class:`arbor.simulation` instead of a :class:`arbor.single_cell_model`.
+
+.. Note::
+
+ **Concepts covered in this example:**
+
+ 1. Building a :class:`arbor.recipe`.
+ 2. Building an :class:`arbor.context` and a :class:`arbor.domain_decomposition`
+ 3. Using the recipe, context and domain decomposition to create a :class:`arbor.simulation`
+ 4. Running the simulation and visualizing the results,
+
+
+Recipes are an important concept in Arbor. They represent the most versatile tool
+for building a complex network of cells. We will go though this example of a model
+of a single cell, before using the recipe to represent more complex networks in
+subsequent examples.
+
+We outline the following steps of this example:
+
+1. Define the **cell**. This is the same cell we have seen before.
+2. Define the **recipe** of the model.
+3. Define the **execution context** of the model: a description of the underlying system
+ on which the simulation will run.
+4. Define the **domain decomposition** of the network: how the cells are distributed on
+ the different ranks of the system.
+5. Define the **simulation**.
+6. **Run** the simulation.
+7. Collect and visualize the **results**.
+
+The cell
+********
+
+We can immediately paste the cell description code from the
+:ref:`previous example <tutorialsinglecellswc-cell>` where it is explained in detail.
+
+.. code-block:: python
+
+ import arbor
+ from arbor import mechanism as mech
+
+ #(1) Read the morphology from an SWC file.
+
+ morph = arbor.load_swc_arbor("morph.swc")
+
+ #(2) Create and populate the label dictionary.
+
+ labels = arbor.label_dict()
+
+ # Regions:
+
+ labels['soma'] = '(tag 1)'
+ labels['axon'] = '(tag 2)'
+ labels['dend'] = '(tag 3)'
+ labels['last'] = '(tag 4)'
+
+ labels['all'] = '(all)'
+
+ labels['gt_1.5'] = '(radius-ge (region "all") 1.5)'
+ labels['custom'] = '(join (region "last") (region "gt_1.5"))'
+
+ # Locsets:
+
+ labels['root'] = '(root)'
+ labels['terminal'] = '(terminal)'
+ labels['custom_terminal'] = '(restrict (locset "terminal") (region "custom"))'
+ labels['axon_terminal'] = '(restrict (locset "terminal") (region "axon"))'
+
+ # (3) Create and populate the decor.
+
+ decor = arbor.decor()
+
+ # Set the default properties of the cell (this overrides the model defaults)
+
+ decor.set_property(Vm =-55)
+
+ # Override the cell defaults.
+
+ decor.paint('"custom"', tempK=270)
+ decor.paint('"soma"', Vm=-50)
+
+ # Paint density mechanisms.
+
+ decor.paint('"all"', 'pas')
+ decor.paint('"custom"', 'hh')
+ decor.paint('"dend"', mech('Ih', {'gbar': 0.001}))
+
+ # Place stimuli and spike detectors.
+
+ decor.place('"root"', arbor.iclamp(10, 1, current=2))
+ decor.place('"root"', arbor.iclamp(30, 1, current=2))
+ decor.place('"root"', arbor.iclamp(50, 1, current=2))
+ decor.place('"axon_terminal"', arbor.spike_detector(-10))
+
+ # Set cv_policy
+
+ soma_policy = arbor.cv_policy_single('"soma"')
+ dflt_policy = arbor.cv_policy_max_extent(1.0)
+ policy = dflt_policy | soma_policy
+ decor.discretization(policy)
+
+ # (4) Create the cell.
+
+ cell = arbor.cable_cell(morph, labels, decor)
+
+We will add one more thing to this section. We will create the voltage probe at the "custom_terminal" locset.
+In the previous example, this probe was registered directly using the :class:`arbor.single_cell_model` object.
+Now it has to be explicitly created and registered in the recipe.
+
+.. _tutorialsinglecellrecipe-probe:
+.. code-block:: python
+
+ probe = arbor.cable_probe_membrane_voltage('"custom_terminal"')
+
+The recipe
+**********
+
+The :class:`arbor.single_cell_model` of the previous example created a :class:`arbor.recipe` under
+the hood, and abstracted away the details so we were unaware of its existence. In this example, we will
+examine the recipe in detail: how to create one, and why it is needed.
+
+.. code-block:: python
+
+ # (1) Create a class that inherits from arbor.recipe
+ class single_recipe (arbor.recipe):
+
+ # (2) Define the class constructor
+ def __init__(self, cell, probes):
+ # The base C++ class constructor must be called first, to ensure that
+ # all memory in the C++ class is initialized correctly.
+ arbor.recipe.__init__(self)
+ self.the_cell = cell
+ self.the_probes = probes
+
+ self.the_cat = arbor.default_catalogue()
+ self.the_cat.extend(arbor.allen_catalogue(), "")
+
+ self.the_props = arbor.cable_global_properties()
+ self.the_props.set_property(Vm=-65, tempK=300, rL=35.4, cm=0.01)
+ self.the_props.set_ion(ion='na', int_con=10, ext_con=140, rev_pot=50, method='nernst/na')
+ self.the_props.set_ion(ion='k', int_con=54.4, ext_con=2.5, rev_pot=-77)
+ self.the_props.set_ion(ion='ca', int_con=5e-5, ext_con=2, rev_pot=132.5)
+
+ self.the_props.register(self.the_cat)
+
+ # (3) Override the num_cells method
+ def num_cells(self):
+ return 1
+
+ # (4) Override the num_sources method
+ def num_sources(self, gid):
+ return 1
+
+ # (5) Override the num_targets method
+ def num_targets(self, gid):
+ return 0
+
+ # (6) Override the num_targets method
+ def cell_kind(self, gid):
+ return arbor.cell_kind.cable
+
+ # (7) Override the cell_description method
+ def cell_description(self, gid):
+ return self.the_cell
+
+ # (8) Override the probes method
+ def probes(self, gid):
+ return self.the_probes
+
+ # (9) Override the connections_on method
+ def connections_on(self, gid):
+ return []
+
+ # (10) Override the gap_junction_on method
+ def gap_junction_on(self, gid):
+ return []
+
+ # (11) Override the event_generators method
+ def event_generators(self, gid):
+ return []
+
+ # (12) Overrode the global_properties method
+ def global_properties(self, gid):
+ return self.the_props
+
+Let's go through the recipe point by point.
+
+Step **(1)** creates a ``single_recipe`` class that inherits from :class:`arbor.recipe`. The base recipe
+implements all the methods defined above with default values except :meth:`arbor.recipe.num_cells`,
+:meth:`arbor.recipe.cell_kind` and :meth:`arbor.recipe.cell_description` which always have to be implemented
+by the user. The :meth:`arbor.recipe.gloabl_properties` also needs to be implemented for
+:class:`arbor.cell_kind.cable` cells. The inherited recipe can implement any number of additional methods and
+have any number of instance or class variables.
+
+Step **(2)** defines the class constructor. In this case, we pass a ``cell`` and a set of ``probes`` as
+arguments. These will be used to initialize the instance variables ``self.the_cell`` and ``self.the_probes``,
+which will be used in the overloaded ``cell_description`` and ``get_probes`` methods. Before variable
+initialization, we call the base C++ class constructor ``arbor.recipe.__init__(self)``. This ensures correct
+initialization of memory in the C++ class.
+
+We also create the ``self.the_cat`` variable and set it to arbor's default mechanism catalogue. This will expose
+the *hh* and *pas* mechanisms but not the *Ih* mechanism, which is present in the allen catalogue. To be able
+to use *Ih*, we extend ``self.the_cat`` to include the allen catalogue.
+
+Finally we create the ``self.the_props`` variable. This will hold the global properties of the model, which apply
+to all the cells in the network. Initially it is empty. We set all the properties of the system similar to
+what we did in the :ref:`previous example <tutorialsinglecellswc-gprop>`. One last important step is to register
+``self.the_cat`` with ``self.the_props``.
+
+.. Note::
+
+ The mechanism catalogue needs to live in the recipe as an instance variable. Its lifetime needs to extend
+ to the entire duration of the simulation.
+
+Step **(3)** overrides the :meth:`arbor.recipe.num_cells` method. It takes 0 arguments. We simply return 1,
+as we are only simulating one cell in this example.
+
+Step **(4)** overrides the :meth:`arbor.recipe.num_sources` method. It takes one argument: ``gid``.
+Given this global ID of a cell, the method will return the number of spike *sources* on the cell. We have defined
+our cell with one spike detector, on one location on the morphology, so we return 1.
+
+Step **(5)** overrides the :meth:`arbor.recipe.num_targets` method. It takes one argument: ``gid``.
+Given the gid, this method returns the number of *targets* on the cell. These are typically synapses on the cell
+that are capable of receiving events from other cells. We have defined our cell with 0 synapses, so we return 0.
+
+Step **(6)** overrides the :meth:`arbor.recipe.cell_kind` method. It takes one argument: ``gid``.
+Given the gid, this method returns the kind of the cell. Our defined cell is a
+:class:`arbor.cell_kind.cable`, so we simply return that.
+
+Step **(7)** overrides the :meth:`arbor.recipe.cell_description` method. It takes one argument: ``gid``.
+Given the gid, this method returns the cell description which is the cell object passed to the constructor
+of the recipe. We return ``self.the_cell``.
+
+Step **(8)** overrides the :meth:`arbor.recipe.get_probes` method. It takes one argument: ``gid``.
+Given the gid, this method returns all the probes on the cell. The probes can be of many different kinds
+measuring different quantities on different locations of the cell. We pass these probes explicitly to the recipe
+and they are stored in ``self.the_probes``, so we return that variable.
+
+Step **(9)** overrides the :meth:`arbor.recipe.connections_on` method. It takes one argument: ``gid``.
+Given the gid, this method returns all the connections ending on that cell. These are typically synapse
+connections from other cell *sources* to specific *targets* on the cell with id ``gid``. Since we are
+simulating a single cell, and self-connections are not possible, we return an empty list.
+
+Step **(10)** overrides the :meth:`arbor.recipe.gap_junctions_on` method. It takes one argument: ``gid``.
+Given the gid, this method returns all the gap junctions on that cell. Gap junctions require 2 separate cells.
+Since we are simulating a single cell, we return an empty list.
+
+Step **(11)** overrides the :meth:`arbor.recipe.event_generators` method. It takes one argument: ``gid``.
+Given the gid, this method returns *event generators* on that cell. These generators trigger events (or
+spikes) on specific *targets* on the cell. They can be used to simulate spikes from other cells, to kick-start
+a simulation for example. Our cell uses a current clamp as a stimulus, and has no targets, so we return
+an empty list.
+
+Step **(12)** overrides the :meth:`arbor.recipe.global_properties` method. It takes one argument: ``kind``.
+This method returns the default global properties of the model which apply to all cells in the network of
+that kind. We return ``self.the_props`` which we defined in step **(1)**.
+
+.. Note::
+
+ You may wonder why the methods: :meth:`arbor.recipe.num_sources`, :meth:`arbor.recipe.num_targets`,
+ and :meth:`arbor.recipe.cell_kind` are required, since they can be inferred by examining the cell description.
+ The recipe was designed to allow building simulations efficiently in a distributed system with minimum
+ communication. Some parts of the model initialization require only the cell kind, or the number of
+ sources and targets, not the full cell description which can be quite expensive to build. Providing these
+ descriptions separately saves time and resources for the user.
+
+ More information on the recipe can be found :ref:`here <modelrecipe>`.
+
+Now we can intantiate a ``single_recipe`` object using the ``cell`` and ``probe`` we created in the
+previous section:
+
+.. code-block:: python
+
+ # Instantiate recipe
+ # Pass the probe in a list because that it what single_recipe expects.
+ recipe = single_recipe(cell, [probe])
+
+The execution context
+*********************
+
+The execution context contains all system-specific information needed by the simulation: it contains the
+thread pool which handles multi-threaded optimization on the CPU; it knows the relevant GPU attributes
+if a GPU is available; and it holds the MPI communicator for distributed simulations. In the previous
+examples, the :class:`arbor.single_cell_model` object created the execution context :class:`arbor.context`
+behind the scenes.
+
+The details of the execution context can be customized by the user. We may specify the number of threads
+in the thread pool; determine the id of the GPU to be used; or create our own MPI communicator. However,
+the ideal settings can usually be inferred from the system, and arbor can do that with a simple command.
+
+.. code-block:: python
+
+ context = arbor.context()
+
+The domain decomposition
+************************
+
+The domain decomposition describes the distribution of the cells over the available computational resources.
+The :class:`arbor.single_cell_model` also handled that without our knowledge in the previous examples.
+Now, we have to define it ourselves.
+
+The :class:`arbor.domain_decomposition` class can be manually created by the user, by deciding which cells
+go on which ranks. Or we can use a load balancer that can partition the cells across ranks according to
+some rules. Arbor provides :class:`arbor.partition_load_balance`, which, using the recipe and execution
+context, creates the :class:`arbor.domain_decomposition` object for us.
+
+Our example is a simple one, with just one cell. We don't need any sophisticated partitioning algorithms, so
+we can use the load balancer, which does a good job distributing simple networks.
+
+.. code-block:: python
+
+ domains = arbor.partition_load_balance(recipe, context)
+
+The simulation
+**************
+
+Finally we have the 3 components needed to create a :class:`arbor.simulation` object.
+
+.. code-block:: python
+
+ sim = arbor.simulation(recipe, domains, context)
+
+Before we run the simulation, however, we need to register what results we expect once execution is over.
+This was handled by the :class:`arbor.single_cell_model` object in the previous example.
+
+We would like to get a list of the spikes on the cell during the runtime of the simulation, and we would like
+to plot the voltage registered by the probe on the "custom_terminal" locset.
+
+.. code-block:: python
+
+ # Instruct the simulation to record the spikes
+ sim.record(arbor.spike_recording.all)
+
+ # Instruct the simulation to sample the probe (0, 0)
+ # at a regular schedule with period = 0.02 ms (50000 Hz)
+ probe_id = arbor.cell_member(0,0)
+ handle = sim.sample(probe_id, arbor.regular_schedule(0.02))
+
+The lines handling probe sampling warrant a second look. First, we declared ``probe_id`` to be a
+:class:`arbor.cell_member`, with :class:`arbor.cell_member.gid` = 0 and :class:`arbor.cell_member.index` = 0.
+This variable serves as a global identifier of a probe on a cell, namely the first declared probe on the
+cell with gid = 0, which is id of the :ref:`only probe <tutorialsinglecellrecipe-probe>` we created on
+the only cell in the model.
+
+Next, we instructed the simulation to sample ``probe_id`` at a frequency of 50KHz. That function returns a
+``handle`` which we will use to extract the results of the sampling after running the simulation.
+
+The execution
+*************
+
+We can now run the simulation we just instantiated for a duration of 100ms with a time step of 0.025 ms.
+
+.. code-block:: python
+
+ sim.run(tfinal=100, dt=0.025)
+
+
+The results
+***********
+
+The last step is result collection. We instructed the simulation to record the spikes on the cell, and
+to sample the probe.
+
+We can print the times of the spikes:
+
+.. code-block:: python
+
+ spikes = sim.spikes()
+
+ # Print the number of spikes.
+ print(len(spikes), 'spikes recorded:')
+
+ # Print the spike times.
+ for s in spikes:
+ print(s)
+
+The probe results, again, warrant some more explanation:
+
+.. code-block:: python
+
+ data = []
+ meta = []
+ for d, m in sim.samples(handle):
+ data.append(d)
+ meta.append(m)
+
+``sim.samples()`` takes a ``handle`` of the probe we wish to examine. It returns a list
+of ``(data, meta)`` terms: ``data`` being the time and value series of the probed quantity; and
+``meta`` being the location of the probe. The size of the returned list depends on the number of
+discrete locations pointed to by the handle. We placed the probe on the "custom_terminal" locset which is
+represented by 2 locations on the morphology. We therefore expect the length of ``sim.samples(handle)``
+to be 2.
+
+We plot the results using pandas and seaborn as we did in the previous example, and expect the same results:
+
+.. code-block:: python
+
+ df = pandas.DataFrame()
+ for i in range(len(data)):
+ df = df.append(pandas.DataFrame({'t/ms': data[i][:, 0], 'U/mV': data[i][:, 1], 'Location': str(meta[i])}))
+ seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable",ci=None).savefig('single_cell_detailed_recipe_result.svg')
+
+The following plot is generated. Identical to the plot of the previous example.
+
+.. figure:: single_cell_detailed_result.svg
+ :width: 400
+ :align: center
+
+
+The full code
+*************
+
+You can find the full code of the example at ``python/examples/single_cell_detailed_recipe.py``.
\ No newline at end of file
diff --git a/doc/tutorial/single_cell_detailed_result.svg b/doc/tutorial/single_cell_detailed_result.svg
new file mode 100644
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diff --git a/doc/tutorial/single_cell_model.rst b/doc/tutorial/single_cell_model.rst
index 5e98fbd5095665d08b4c986576756c127848f1a5..ac6acdf3aa66e6aa7927b403bbdb3510cb49261e 100644
--- a/doc/tutorial/single_cell_model.rst
+++ b/doc/tutorial/single_cell_model.rst
@@ -1,23 +1,24 @@
-.. _gs_single_cell:
+.. _tutorialsimplecell:
-A single cell model
-================================
+A simple single cell model
+==========================
Building and testing detailed models of individual cells, then optimizing their
parameters is usually the first step in building models with multi-compartment cells.
Arbor supports a *single cell model* workflow for this purpose, which is a good way to
introduce Arbor's cell modelling concepts and approach.
-This guide will walk through a series of single cell models of increasing complexity.
-Links are provided to separate documentation that covers relevant topics in more detail.
+.. Note::
-In an interactive Python interpreter, you can use ``help()`` on any class or function to
-obtain some documentation. (Try, for example, ``help(arbor.simulation``).
+ **Concepts covered in this example:**
-.. _single_soma:
+ 1. Intro to building a morphology from a :class:`arbor.segment_tree`.
+ 2. Intro to region and locset expressions.
+ 3. Intro to decors and cell decorations.
+ 4. Building a :class:`arbor.cable_cell` object.
+ 5. Building a :class:`arbor.single_cell_model` object.
+ 6. Running a simulation and visualising the results.
-Single segment cell with HH dynamics
-----------------------------------------------------
The most trivial representation of a cell in Arbor is to model the entire cell as a
single cylinder. The following example shows the steps required to construct a model of a
diff --git a/python/example/morph.swc b/python/example/morph.swc
new file mode 100644
index 0000000000000000000000000000000000000000..597322185d60f9a6d5f424246495028aaba568e0
--- /dev/null
+++ b/python/example/morph.swc
@@ -0,0 +1,15 @@
+# id, tag, x, y, z, r, parent
+ 1 1 0.0 0.0 0.0 2.0 -1 # seg0 prox / seg9 prox
+ 2 1 40.0 0.0 0.0 2.0 1 # seg0 dist
+ 3 3 40.0 0.0 0.0 0.8 2 # seg1 prox
+ 4 3 80.0 0.0 0.0 0.8 3 # seg1 dist / seg2 prox
+ 5 3 120.0 -5.0 0.0 0.8 4 # seg2 dist / seg3 prox
+ 6 3 200.0 40.0 0.0 0.4 5 # seg3 dist / seg4 prox
+ 7 3 260.0 60.0 0.0 0.2 6 # seg4 dist
+ 8 3 120.0 -5.0 0.0 0.5 5 # seg5 prox
+ 9 3 190.0 -30.0 0.0 0.5 8 # seg5 dist / seg6 prox / seg7 prox
+ 10 4 240.0 -70.0 0.0 0.2 9 # seg6 dist
+ 11 4 230.0 -10.0 0.0 0.2 9 # seg7 dist / seg8 prox
+ 12 4 360.0 -20.0 0.0 0.2 11 # seg8 dist
+ 13 2 -70.0 0.0 0.0 0.4 1 # seg9 dist / seg10 prox
+ 14 2 -100.0 0.0 0.0 0.4 13 # seg10 dist
diff --git a/python/example/single_cell_detailed.py b/python/example/single_cell_detailed.py
new file mode 100755
index 0000000000000000000000000000000000000000..140b4d900cf6880aa1e36e1892997f2e29e0c067
--- /dev/null
+++ b/python/example/single_cell_detailed.py
@@ -0,0 +1,116 @@
+import arbor
+import pandas
+import seaborn
+import sys
+from arbor import mechanism as mech
+
+#(1) Read the morphology from an SWC file.
+
+# Read the SWC filename from input
+# Example from docs: morph.swc
+
+if len(sys.argv) < 2:
+ print("No SWC file passed to the program")
+ sys.exit(0)
+
+filename = sys.argv[1]
+morph = arbor.load_swc_arbor(filename)
+
+#(2) Create and populate the label dictionary.
+
+labels = arbor.label_dict()
+
+# Regions:
+
+labels['soma'] = '(tag 1)'
+labels['axon'] = '(tag 2)'
+labels['dend'] = '(tag 3)'
+labels['last'] = '(tag 4)'
+
+labels['all'] = '(all)'
+
+labels['gt_1.5'] = '(radius-ge (region "all") 1.5)'
+labels['custom'] = '(join (region "last") (region "gt_1.5"))'
+
+# Locsets:
+
+labels['root'] = '(root)'
+labels['terminal'] = '(terminal)'
+labels['custom_terminal'] = '(restrict (locset "terminal") (region "custom"))'
+labels['axon_terminal'] = '(restrict (locset "terminal") (region "axon"))'
+
+# (3) Create and populate the decor.
+
+decor = arbor.decor()
+
+# Set the default properties of the cell (this overrides the model defaults).
+
+decor.set_property(Vm =-55)
+
+# Override the cell defaults.
+
+decor.paint('"custom"', tempK=270)
+decor.paint('"soma"', Vm=-50)
+
+# Paint density mechanisms.
+
+decor.paint('"all"', 'pas')
+decor.paint('"custom"', 'hh')
+decor.paint('"dend"', mech('Ih', {'gbar': 0.001}))
+
+# Place stimuli and spike detectors.
+
+decor.place('"root"', arbor.iclamp(10, 1, current=2))
+decor.place('"root"', arbor.iclamp(30, 1, current=2))
+decor.place('"root"', arbor.iclamp(50, 1, current=2))
+decor.place('"axon_terminal"', arbor.spike_detector(-10))
+
+# Set cv_policy
+
+soma_policy = arbor.cv_policy_single('"soma"')
+dflt_policy = arbor.cv_policy_max_extent(1.0)
+policy = dflt_policy | soma_policy
+decor.discretization(policy)
+
+# (4) Create the cell.
+
+cell = arbor.cable_cell(morph, labels, decor)
+
+# (5) Construct the model
+
+model = arbor.single_cell_model(cell)
+
+# (6) Set the model default properties
+
+model.properties.set_property(Vm =-65, tempK=300, rL=35.4, cm=0.01)
+model.properties.set_ion('na', int_con=10, ext_con=140, rev_pot=50, method='nernst/na')
+model.properties.set_ion('k', int_con=54.4, ext_con=2.5, rev_pot=-77)
+
+# Extend the default catalogue with the allen catalogue.
+
+model.catalogue.extend(arbor.allen_catalogue(), "")
+
+# (7) Add probes.
+
+model.probe('voltage', where='"custom_terminal"', frequency=50000)
+
+# (8) Run the simulation.
+
+model.run(tfinal=100, dt=0.025)
+
+# (9) Print the spikes.
+
+print(len(model.spikes), 'spikes recorded:')
+
+# Print the spike times.
+
+for s in model.spikes:
+ print(s)
+
+# (10) Plot the voltages
+
+df = pandas.DataFrame()
+for t in model.traces:
+ df=df.append(pandas.DataFrame({'t/ms': t.time, 'U/mV': t.value, 'Location': str(t.location), 'Variable': t.variable}))
+
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable",ci=None).savefig('single_cell_detailed_result.svg')
diff --git a/python/example/single_cell_detailed_recipe.py b/python/example/single_cell_detailed_recipe.py
new file mode 100644
index 0000000000000000000000000000000000000000..46a9cd6410dd5e2cf9212ba3ee1252a789983e3d
--- /dev/null
+++ b/python/example/single_cell_detailed_recipe.py
@@ -0,0 +1,178 @@
+import arbor
+import pandas
+import seaborn
+import sys
+from arbor import mechanism as mech
+
+#(1) Creat a cell.
+
+# Create the morphology
+
+# Read the SWC filename from input
+# Example from docs: morph.swc
+
+if len(sys.argv) < 2:
+ print("No SWC file passed to the program")
+ sys.exit(0)
+
+filename = sys.argv[1]
+morph = arbor.load_swc_arbor(filename)
+
+# Create and populate the label dictionary.
+
+labels = arbor.label_dict()
+
+# Regions:
+
+labels['soma'] = '(tag 1)'
+labels['axon'] = '(tag 2)'
+labels['dend'] = '(tag 3)'
+labels['last'] = '(tag 4)'
+
+labels['all'] = '(all)'
+
+labels['gt_1.5'] = '(radius-ge (region "all") 1.5)'
+labels['custom'] = '(join (region "last") (region "gt_1.5"))'
+
+# Locsets:
+
+labels['root'] = '(root)'
+labels['terminal'] = '(terminal)'
+labels['custom_terminal'] = '(restrict (locset "terminal") (region "custom"))'
+labels['axon_terminal'] = '(restrict (locset "terminal") (region "axon"))'
+
+# Create and populate the decor.
+
+decor = arbor.decor()
+
+# Set the default properties.
+
+decor.set_property(Vm =-55)
+
+# Override the defaults.
+
+decor.paint('"custom"', tempK=270)
+decor.paint('"soma"', Vm=-50)
+
+# Paint density mechanisms.
+
+decor.paint('"all"', 'pas')
+decor.paint('"custom"', 'hh')
+decor.paint('"dend"', mech('Ih', {'gbar': 0.001}))
+
+# Place stimuli and spike detectors.
+
+decor.place('"root"', arbor.iclamp(10, 1, current=2))
+decor.place('"root"', arbor.iclamp(30, 1, current=2))
+decor.place('"root"', arbor.iclamp(50, 1, current=2))
+decor.place('"axon_terminal"', arbor.spike_detector(-10))
+
+# Set cv_policy
+
+soma_policy = arbor.cv_policy_single('"soma"')
+dflt_policy = arbor.cv_policy_max_extent(1.0)
+policy = dflt_policy | soma_policy
+decor.discretization(policy)
+
+# Create a cell
+
+cell = arbor.cable_cell(morph, labels, decor)
+
+# (2) Declare a probe.
+
+probe = arbor.cable_probe_membrane_voltage('"custom_terminal"')
+
+# (3) Create a recipe class and instantiate a recipe
+
+class single_recipe (arbor.recipe):
+
+ def __init__(self, cell, probes):
+ # The base C++ class constructor must be called first, to ensure that
+ # all memory in the C++ class is initialized correctly.
+ arbor.recipe.__init__(self)
+ self.the_cell = cell
+ self.the_probes = probes
+
+ self.the_cat = arbor.default_catalogue()
+ self.the_cat.extend(arbor.allen_catalogue(), "")
+
+ self.the_props = arbor.cable_global_properties()
+ self.the_props.set_property(Vm=-65, tempK=300, rL=35.4, cm=0.01)
+ self.the_props.set_ion(ion='na', int_con=10, ext_con=140, rev_pot=50, method='nernst/na')
+ self.the_props.set_ion(ion='k', int_con=54.4, ext_con=2.5, rev_pot=-77)
+ self.the_props.set_ion(ion='ca', int_con=5e-5, ext_con=2, rev_pot=132.5)
+
+ self.the_props.register(self.the_cat)
+
+ def num_cells(self):
+ return 1
+
+ def num_sources(self, gid):
+ return 1
+
+ def num_targets(self, gid):
+ return 0
+
+ def cell_kind(self, gid):
+ return arbor.cell_kind.cable
+
+ def cell_description(self, gid):
+ return self.the_cell
+
+ def probes(self, gid):
+ return self.the_probes
+
+ def connections_on(self, gid):
+ return []
+
+ def gap_junction_on(self, gid):
+ return []
+
+ def event_generators(self, gid):
+ return []
+
+ def global_properties(self, gid):
+ return self.the_props
+
+recipe = single_recipe(cell, [probe])
+
+# (4) Create an execution context
+
+context = arbor.context()
+
+# (5) Create a domain decomposition
+
+domains = arbor.partition_load_balance(recipe, context)
+
+# (6) Create a simulation
+
+sim = arbor.simulation(recipe, domains, context)
+
+# Instruct the simulation to record the spikes and sample the probe
+
+sim.record(arbor.spike_recording.all)
+
+probe_id = arbor.cell_member(0,0)
+handle = sim.sample(probe_id, arbor.regular_schedule(0.02))
+
+# (7) Run the simulation
+
+sim.run(tfinal=100, dt=0.025)
+
+# (8) Print or display the results
+
+spikes = sim.spikes()
+print(len(spikes), 'spikes recorded:')
+for s in spikes:
+ print(s)
+
+data = []
+meta = []
+for d, m in sim.samples(handle):
+ data.append(d)
+ meta.append(m)
+
+df = pandas.DataFrame()
+for i in range(len(data)):
+ df = df.append(pandas.DataFrame({'t/ms': data[i][:, 0], 'U/mV': data[i][:, 1], 'Location': str(meta[i]), 'Variable':'voltage'}))
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable",ci=None).savefig('single_cell_recipe_result.svg')
\ No newline at end of file
diff --git a/python/example/single_cell_swc.py b/python/example/single_cell_swc.py
index 7133178c4b57a509a0820c549570fcc92c34f6cc..5584b9f5d41176679339ef2a9763cb05113b5cda 100755
--- a/python/example/single_cell_swc.py
+++ b/python/example/single_cell_swc.py
@@ -23,7 +23,7 @@ if len(sys.argv) < 2:
sys.exit(0)
filename = sys.argv[1]
-morpho = arbor.load_swc(filename)
+morpho = arbor.load_swc_arbor(filename)
# Define the regions and locsets in the model.
defs = {'soma': '(tag 1)', # soma has tag 1 in swc files.
diff --git a/python/morphology.cpp b/python/morphology.cpp
index 0412b3d0ec74cb7d76c775bf24878dbfbbbb9663..fb999543fe493d9cb5f51312cff24e2ab84afaf1 100644
--- a/python/morphology.cpp
+++ b/python/morphology.cpp
@@ -234,7 +234,7 @@ void register_morphology(py::module& m) {
// Function that creates a morphology from an swc file.
// Wraps calls to C++ functions arborio::parse_swc() and arborio::load_swc_arbor().
- m.def("load_swc",
+ m.def("load_swc_arbor",
[](std::string fname) {
std::ifstream fid{fname};
if (!fid.good()) {
diff --git a/scripts/travis/build.sh b/scripts/travis/build.sh
index 316594b01b586a07daa0d75da2ac661e3401e315..f3e09600f3e94e8d288eab1be74beaee846a3fa5 100755
--- a/scripts/travis/build.sh
+++ b/scripts/travis/build.sh
@@ -107,17 +107,21 @@ fi
if [[ "${WITH_PYTHON}" == "true" ]]; then
progress "Building python module"
- make pyarb -j4 || error "building pyarb"
+ make pyarb -j4 || error "building pyarb"
progress "Python unit tests"
- python$PY $python_path/test/unit/runner.py -v2 || error "running python unit tests (serial)"
+ python$PY $python_path/test/unit/runner.py -v2 || error "running python unit tests (serial)"
progress "Python example: network_ring"
- python$PY $python_path/example/network_ring.py || error "running python network_ring example"
+ python$PY $python_path/example/network_ring.py || error "running python network_ring example"
progress "Python example: single_cell_model"
- python$PY $python_path/example/single_cell_model.py || error "running python single_cell_model example"
+ python$PY $python_path/example/single_cell_model.py || error "running python single_cell_model example"
progress "Python example: single_cell_recipe"
- python$PY $python_path/example/single_cell_recipe.py || error "running python single_cell_recipe example"
+ python$PY $python_path/example/single_cell_recipe.py || error "running python single_cell_recipe example"
+ progress "Python example: single_cell_detailed"
+ python$PY $python_path/example/single_cell_detailed.py $python_path/example/morph.swc || error "running python single_cell_detailed example"
+ progress "Python example: single_cell_detailed_recipe"
+ python$PY $python_path/example/single_cell_detailed_recipe.py $python_path/example/morph.swc || error "running python single_cell_detailed_recipe example"
progress "Python example: single_cell_swc"
- python$PY $python_path/example/single_cell_swc.py $base_path/test/unit/swc/pyramidal.swc || error "running python single_cell_swc example"
+ python$PY $python_path/example/single_cell_swc.py $base_path/test/unit/swc/pyramidal.swc || error "running python single_cell_swc example"
if [[ "${WITH_DISTRIBUTED}" = "mpi" ]]; then
if [[ "$TRAVIS_OS_NAME" = "osx" ]]; then
progress "Python distributed unit tests (MPI)"