From da8450ec757a28702a07971fe5c80b9ea939c036 Mon Sep 17 00:00:00 2001
From: lukasgd <38319063+lukasgd@users.noreply.github.com>
Date: Tue, 25 Oct 2022 20:41:57 +0200
Subject: [PATCH] Arbor cable cell exporter and backend support in BluePyOpt
(#1959)
* Support for exporting models with parameters optimized in BluePyOpt to a mixed JSON/ACC-format that can be loaded in Arbor (with faithful axon-replacement modelling)
* A tutorial and examples on how to run simulations with such models including a neocortical layer-5 pyramidal cell exported from BluePyOpt
* Support for using Arbor as a backend for single cell optimization in BluePyOpt
---
arbor/include/arbor/morph/morphology.hpp | 5 +
arbor/include/arbor/morph/segment_tree.hpp | 6 +-
arbor/morph/morphology.cpp | 23 +
arbor/morph/segment_tree.cpp | 90 +-
doc/concepts/decor.rst | 14 +-
doc/concepts/interconnectivity.rst | 2 +-
doc/concepts/labels.rst | 6 +-
doc/concepts/morphology.rst | 9 +-
doc/cpp/hardware.rst | 2 +-
doc/cpp/morphology.rst | 5 +
doc/dev/simd_api.rst | 2 +-
doc/python/morphology.rst | 6 +
doc/tutorial/index.rst | 1 +
doc/tutorial/network_ring_gpu.rst | 2 +-
doc/tutorial/network_ring_mpi.rst | 2 +-
doc/tutorial/probe_lfpykit.rst | 2 +-
doc/tutorial/single_cell_bluepyopt.rst | 305 +
...le_cell_bluepyopt_gui_l5pc_bAP_attempt.png | Bin 0 -> 395879 bytes
...ngle_cell_bluepyopt_gui_l5pc_bAP_final.png | Bin 0 -> 430411 bytes
...ingle_cell_bluepyopt_gui_l5pc_overview.png | Bin 0 -> 368386 bytes
.../single_cell_bluepyopt_l5pc_bAP_dend1.svg | 1046 +
.../single_cell_bluepyopt_simplecell.svg | 1153 +
python/cable_cell_io.cpp | 21 +-
.../single_cell_bluepyopt/l5pc/C060114A7.asc | 16307 ++++++++++++
.../l5pc/C060114A7_axon_replacement.acc | 21 +
.../l5pc/C060114A7_modified.acc | 21702 ++++++++++++++++
.../single_cell_bluepyopt/l5pc/Readme.md | 13 +
.../single_cell_bluepyopt/l5pc/l5pc.json | 11 +
.../single_cell_bluepyopt/l5pc/l5pc_decor.acc | 37 +
.../l5pc/l5pc_label_dict.acc | 9 +
.../simplecell/Readme.md | 14 +
.../simplecell/simple.swc | 4 +
.../simplecell/simple_cell.json | 9 +
.../simplecell/simple_cell_decor.acc | 5 +
.../simplecell/simple_cell_label_dict.acc | 9 +
python/example/single_cell_bluepyopt_l5pc.py | 133 +
.../single_cell_bluepyopt_simplecell.py | 85 +
python/morphology.cpp | 12 +
python/single_cell_model.cpp | 39 +-
test/unit/test_segment_tree.cpp | 40 +
40 files changed, 41101 insertions(+), 51 deletions(-)
create mode 100644 doc/tutorial/single_cell_bluepyopt.rst
create mode 100644 doc/tutorial/single_cell_bluepyopt_gui_l5pc_bAP_attempt.png
create mode 100644 doc/tutorial/single_cell_bluepyopt_gui_l5pc_bAP_final.png
create mode 100644 doc/tutorial/single_cell_bluepyopt_gui_l5pc_overview.png
create mode 100644 doc/tutorial/single_cell_bluepyopt_l5pc_bAP_dend1.svg
create mode 100644 doc/tutorial/single_cell_bluepyopt_simplecell.svg
create mode 100644 python/example/single_cell_bluepyopt/l5pc/C060114A7.asc
create mode 100644 python/example/single_cell_bluepyopt/l5pc/C060114A7_axon_replacement.acc
create mode 100644 python/example/single_cell_bluepyopt/l5pc/C060114A7_modified.acc
create mode 100644 python/example/single_cell_bluepyopt/l5pc/Readme.md
create mode 100644 python/example/single_cell_bluepyopt/l5pc/l5pc.json
create mode 100644 python/example/single_cell_bluepyopt/l5pc/l5pc_decor.acc
create mode 100644 python/example/single_cell_bluepyopt/l5pc/l5pc_label_dict.acc
create mode 100644 python/example/single_cell_bluepyopt/simplecell/Readme.md
create mode 100644 python/example/single_cell_bluepyopt/simplecell/simple.swc
create mode 100644 python/example/single_cell_bluepyopt/simplecell/simple_cell.json
create mode 100644 python/example/single_cell_bluepyopt/simplecell/simple_cell_decor.acc
create mode 100644 python/example/single_cell_bluepyopt/simplecell/simple_cell_label_dict.acc
create mode 100755 python/example/single_cell_bluepyopt_l5pc.py
create mode 100755 python/example/single_cell_bluepyopt_simplecell.py
diff --git a/arbor/include/arbor/morph/morphology.hpp b/arbor/include/arbor/morph/morphology.hpp
index b12f9b98..68ce34a0 100644
--- a/arbor/include/arbor/morph/morphology.hpp
+++ b/arbor/include/arbor/morph/morphology.hpp
@@ -41,6 +41,11 @@ public:
// Range of segments in a branch.
const std::vector<msegment>& branch_segments(msize_t b) const;
+ // Convert to segment tree
+ // Note: tree == arb::morphology(tree).to_segment_tree() is not guaranteed
+ // to be true.
+ segment_tree to_segment_tree() const;
+
friend std::ostream& operator<<(std::ostream&, const morphology&);
};
diff --git a/arbor/include/arbor/morph/segment_tree.hpp b/arbor/include/arbor/morph/segment_tree.hpp
index 7d59be59..0d9c6b56 100644
--- a/arbor/include/arbor/morph/segment_tree.hpp
+++ b/arbor/include/arbor/morph/segment_tree.hpp
@@ -85,4 +85,8 @@ equivalent(const segment_tree& a,
ARB_ARBOR_API segment_tree
apply(const segment_tree&, const isometry&);
-} // namesapce arb
+// Roots of regions of specific tag in segment tree
+ARB_ARBOR_API std::vector<msize_t> tag_roots(const segment_tree& in, int tag);
+
+
+} // namespace arb
\ No newline at end of file
diff --git a/arbor/morph/morphology.cpp b/arbor/morph/morphology.cpp
index 34159271..3ee1fdfd 100644
--- a/arbor/morph/morphology.cpp
+++ b/arbor/morph/morphology.cpp
@@ -173,6 +173,29 @@ msize_t morphology::num_branches() const {
return impl_->branches_.size();
}
+ARB_ARBOR_API segment_tree morphology::to_segment_tree() const {
+ segment_tree st;
+ const auto& branches = impl_->branches_;
+
+ for (auto bi: make_span(branches.size()) ) {
+ const auto& branch = branches[bi];
+ for (auto si: make_span(branch.size())) {
+ const auto& seg = branch[si];
+ auto p = mnpos;
+ if (si == 0) {
+ auto bp = impl_->branch_parents_[bi];
+ p = bp == mnpos ? mnpos : branches[bp].back().id;
+ } else {
+ p = branch[si-1].id;
+ }
+ st.append(p, seg.prox, seg.dist, seg.tag);
+ }
+ }
+
+ return st;
+}
+
+
ARB_ARBOR_API std::ostream& operator<<(std::ostream& o, const morphology& m) {
return o << *m.impl_;
}
diff --git a/arbor/morph/segment_tree.cpp b/arbor/morph/segment_tree.cpp
index 3249ed5f..d624f5be 100644
--- a/arbor/morph/segment_tree.cpp
+++ b/arbor/morph/segment_tree.cpp
@@ -10,6 +10,8 @@
#include "util/span.hpp"
#include "util/transform.hpp"
+using arb::util::make_span;
+
namespace arb {
struct node_t {
@@ -17,9 +19,9 @@ struct node_t {
msize_t id;
};
-using node_p = std::function<bool(const node_t&)>;
+using id_p = std::function<bool(msize_t)>;
-node_p yes = [](node_t) { return true; };
+id_p yes = [](msize_t) { return true; };
// invert parent <*> child relation, returns a map of parent_id -> [children_id]
// For predictable ordering we sort the vectors.
@@ -30,52 +32,81 @@ std::map<msize_t, std::vector<msize_t>> tree_to_children(const segment_tree& tre
for (auto& [k, v]: result) std::sort(v.begin(), v.end());
return result;
}
-
-// Copy a segment tree into a new tree
+
+// Copy a segment subtree into a new tree
// - tree to be (partially) copied
-// - start={parent, id}: start by attaching segment=`id`` from `tree` to the
-// output at `parent`, then its children to it recursively
+// - start={parent, id}: start by attaching segment=`id` from `tree` to the segment
+// `parent` of `init`, then its children to it recursively
// - predicate: if returning false for a given node, we prune that sub-tree starting
-// at node (inclusive). Can be used to prune trees by parent or id.
+// at node (inclusive). Can be used to prune trees by segment id.
// - init: initial tree to append to
-// Note: this is basically a recursive function w/ an explicit stack.
-segment_tree copy_if(const segment_tree& tree,
- node_t start,
- node_p predicate,
- const segment_tree& init={}) {
+// Note: this is an iterative implementation of depth-first traversal with an explicit stack.
+std::pair<segment_tree, std::vector<bool>> copy_subtree_if(const segment_tree& tree,
+ const node_t& start,
+ id_p predicate,
+ const segment_tree& init={}) {
auto children_of = tree_to_children(tree);
auto& segments = tree.segments();
segment_tree result = init;
+ std::vector<bool> copied(segments.size(), false);
auto todo = std::vector<node_t>{start};
while (!todo.empty()) {
auto node = todo.back();
todo.pop_back();
- if (!predicate(node)) continue;
+ if (!predicate(node.id)) continue;
+ copied[node.id] = true;
const auto& segment = segments[node.id];
auto current = result.append(node.parent, segment.prox, segment.dist, segment.tag);
for (auto child: children_of[node.id]) {
todo.push_back({current, child});
}
}
- return result;
+ return {result, copied};
+}
+
+// Filter a segment tree as a graph
+// - tree to be (partially) filtered
+// - predicate: if returning false for a given node, we exclude that node from the
+// returned tree. Can be used to filter trees by segment id.
+std::pair<segment_tree, std::vector<bool>> copy_fulltree_if(const segment_tree& tree,
+ id_p predicate) {
+ auto& segments = tree.segments();
+ auto& parents = tree.parents();
+
+ segment_tree result;
+ std::vector<bool> copied(segments.size(), false);
+ std::vector<msize_t> new_ids(segments.size(), mnpos);
+
+ for (msize_t id: make_span(0, segments.size())) {
+ if (!predicate(id)) continue;
+ copied[id] = true;
+ const auto& segment = segments[id];
+ auto parent = parents[id];
+ new_ids[id] = result.append(parent != mnpos ? new_ids[parent] : mnpos,
+ segment.prox, segment.dist, segment.tag);
+ }
+ return {result, copied};
}
std::pair<segment_tree, segment_tree>
split_at(const segment_tree& tree, msize_t at) {
if (at >= tree.size() || at == mnpos) throw invalid_segment_parent(at, tree.size());
// span the sub-tree starting at the splitting node
- segment_tree post = copy_if(tree, {mnpos, at}, yes);
- // copy the original tree, but skip all nodes in the `post` tree
- segment_tree pre = copy_if(tree,
- {mnpos, 0},
- [=](auto& node) { return node.id != at; });
+ const auto& post_copied = copy_subtree_if(tree, {mnpos, at}, yes);
+ auto post = post_copied.first;
+ auto copied = post_copied.second;
+
+ // copy the original segment_tree (as a graph), skipping all nodes in the `post` subtree
+ segment_tree pre = copy_fulltree_if(tree,
+ [&](msize_t id) { return !copied[id]; }).first;
+
return {pre, post};
}
segment_tree
join_at(const segment_tree& lhs, msize_t at, const segment_tree& rhs) {
if (at >= lhs.size() && at != mnpos) throw invalid_segment_parent(at, lhs.size());
- return copy_if(rhs, {at, 0}, yes, lhs);
+ return copy_subtree_if(rhs, {at, 0}, yes, lhs).first;
}
bool
@@ -98,7 +129,7 @@ equivalent(const segment_tree& a,
return segs;
};
- std::vector<std::pair<arb::msize_t, arb::msize_t>> todo{{arb::mnpos, arb::mnpos}};
+ std::vector<std::pair<msize_t, msize_t>> todo{{arb::mnpos, arb::mnpos}};
while (!todo.empty()) {
const auto& [a_cursor, b_cursor] = todo.back();
auto as = fetch_children(a_cursor, a.segments(), a_children_of);
@@ -200,5 +231,22 @@ ARB_ARBOR_API std::ostream& operator<<(std::ostream& o, const segment_tree& m) {
<< io::sepval(tstr, ' ') << "))";
}
+
+ARB_ARBOR_API std::vector<msize_t> tag_roots(const segment_tree& t, int tag) {
+ const auto& segments = t.segments();
+ const auto& parents = t.parents();
+ std::vector<msize_t> tag_roots;
+
+ for (auto i: make_span(0, segments.size())) {
+ auto par = parents[i];
+ if (segments[i].tag == tag && (par == mnpos || segments[par].tag != tag)) {
+ tag_roots.push_back(i);
+ }
+ }
+
+ return tag_roots;
+}
+
+
} // namespace arb
diff --git a/doc/concepts/decor.rst b/doc/concepts/decor.rst
index 5cc973f3..a163ca49 100644
--- a/doc/concepts/decor.rst
+++ b/doc/concepts/decor.rst
@@ -342,13 +342,13 @@ A point mechanism (synapse) can form the target of a :term:`connection` on a cel
expsyn = arbor.mechanism('expsyn')
# Wrap the 'expsyn' mechanism in a `synapse` object and add it to the decor.
- decor.paint('"syn_loc_0"', arbor.synapse(expsyn))
+ decor.place('"syn_loc_0"', arbor.synapse(expsyn))
# Create an 'expsyn' mechanism with default parameter values as a `synapse` object, and add it to the decor.
- decor.paint('"syn_loc_1"', arbor.synapse("expsyn"))
+ decor.place('"syn_loc_1"', arbor.synapse("expsyn"))
# Create an 'expsyn' mechanism with modified 'tau' parameter as a `synapse` object, and add it to the decor.
- decor.paint('"syn_loc_2"', arbor.synapse("expsyn", {"tau": 1.0}))
+ decor.place('"syn_loc_2"', arbor.synapse("expsyn", {"tau": 1.0}))
.. _cablecell-threshold-detectors:
@@ -399,10 +399,10 @@ on two separate cells.
gj = arbor.mechanism("gj", {"g": 2.0})
# Wrap the 'gj' mechanism in a `junction` object and add it to the decor.
- decor.paint('"gj_loc_0"', arbor.junction(gj))
+ decor.place('"gj_loc_0"', arbor.junction(gj))
# Create a 'gj' mechanism with modified 'g' parameter as a `junction` object, and add it to the decor.
- decor.paint('"gj_loc_1"', arbor.junction("gj", {"g": 1.5}))
+ decor.place('"gj_loc_1"', arbor.junction("gj", {"g": 1.5}))
.. _cablecell-stimuli:
@@ -431,14 +431,14 @@ constant stimuli and constant amplitude stimuli restricted to a fixed time inter
decor.place('(root)', arbor.iclamp(10), "iclamp0")
# Constant amplitude 10 nA stimulus at 20 Hz, with initial phase of π/4 radians.
- decor.place('(root)', arbor.iclamp(10, frequency=0.020, phasce=math.pi/4), "iclamp1")
+ decor.place('(root)', arbor.iclamp(10, frequency=0.020, phase=math.pi/4), "iclamp1")
# Stimulus at 1 kHz, amplitude 10 nA, for 40 ms starting at t = 30 ms.
decor.place('(root)', arbor.iclamp(30, 40, 10, frequency=1), "iclamp2")
# Piecewise linear stimulus with amplitude ranging from 0 nA to 10 nA,
# starting at t = 30 ms and stopping at t = 50 ms.
- decor.place('(root)', arbor.iclamp([(30, 0), (37, 10), (43, 8), (50, 0)], "iclamp3")
+ decor.place('(root)', arbor.iclamp([(30, 0), (37, 10), (43, 8), (50, 0)]), "iclamp3")
.. _cablecell-probes:
diff --git a/doc/concepts/interconnectivity.rst b/doc/concepts/interconnectivity.rst
index d4dd8d98..f8e289d5 100644
--- a/doc/concepts/interconnectivity.rst
+++ b/doc/concepts/interconnectivity.rst
@@ -68,7 +68,7 @@ full recipe.
Arbor uses a lazily constructed network (from the ``recipe`` callbacks) for
good reason; storing the full connectivity (for all ``gids``) in the
- ``recipe`` can lead to probitivively large memory footprints. Keep this in
+ ``recipe`` can lead to prohibitively large memory footprints. Keep this in
mind when designing your connectivity and heed the consequences of doing I/O
in these callbacks. This is doubly important when using models with dynamic
connectivity where the temptation to store all connections is even larger and
diff --git a/doc/concepts/labels.rst b/doc/concepts/labels.rst
index 2997d38a..ea3151ba 100644
--- a/doc/concepts/labels.rst
+++ b/doc/concepts/labels.rst
@@ -300,7 +300,7 @@ Locset expressions
.. label:: (proximal-translate ls:locset distance:real)
The set of locations that correspond to moving each location in the ``ls`` in the proximal direction
- ``distance`` μm. The locations in the output have a one to one correspondance with those in ``ls``.
+ ``distance`` μm. The locations in the output have a one to one correspondence with those in ``ls``.
.. figure:: ../gen-images/proximal_translate_label.svg
:width: 600
@@ -316,7 +316,7 @@ Locset expressions
An input location will generate multiple output locations when it is translated
past a fork point, with a new location for each child branch (see the example
- below). For this reason there is not a one-to-one correspondance between locations
+ below). For this reason there is not a one-to-one correspondence between locations
in the input and output sets, so the results are sorted and duplicates are removed.
@@ -862,7 +862,7 @@ the regions, locsets or iexpr defined in the label dictionary by referring to th
'root': '(root)', # typically the start of the soma is at the root of the cell.
'stim_site': '(location 0 0.5)', # site for the stimulus, in the middle of branch 0.
'axon_end': '(restrict (terminal) (region "axon"))', # end of the axon.
- 'rad_expr': '(radius 0.5)'} # iexpr evaluating the radius scaled by 0.5
+ 'rad_expr': '(radius 0.5)' # iexpr evaluating the radius scaled by 0.5
})
diff --git a/doc/concepts/morphology.rst b/doc/concepts/morphology.rst
index bcc8b78f..5a71cde9 100644
--- a/doc/concepts/morphology.rst
+++ b/doc/concepts/morphology.rst
@@ -607,7 +607,7 @@ Editing morphologies
While a reified morphology cannot be edited -- it is immutable by definition --
the segment tree can be changed. If you need to make such modifications, first
-consider whethnr they should be stored in a file as this is often easier for
+consider whether they should be stored in a file as this is often easier for
tracking provenance and version history.
For the remaining cases, Arbor offers a limited suite of tools. First, most
@@ -632,6 +632,13 @@ Two common editing operations are provided
Note that ``join_at`` and ``split_at`` are inverse to each other.
+A particular use-case for these operations is pruning a specific tag-region in the
+segment tree and replacing it with a surrogate model. This is e.g. commonly performed
+for the axon, known as axon-replacement. For this purpose, the function ``tag_roots``
+allows to obtain the IDs of root segments of a a tag region. These IDs can
+then be used with ``split_at`` to split off subtrees and ``join_at`` to attach a
+surrogate subtree (with the same parent as the one split off).
+
.. _morph-formats:
Supported file formats
diff --git a/doc/cpp/hardware.rst b/doc/cpp/hardware.rst
index d2e810b3..d4038423 100644
--- a/doc/cpp/hardware.rst
+++ b/doc/cpp/hardware.rst
@@ -90,7 +90,7 @@ The header ``arborenv/concurrency.hpp`` supplies lower-level functions for query
Returns the list of logical processor ids where the calling thread has affinity,
or an empty vector if unable to determine.
-The header ``arborenv/gpu_env.hpp`` supplies lower-level functions for queruing the GPU environment.
+The header ``arborenv/gpu_env.hpp`` supplies lower-level functions for querying the GPU environment.
.. cpp:function:: int find_private_gpu(MPI_Comm comm)
diff --git a/doc/cpp/morphology.rst b/doc/cpp/morphology.rst
index 8800e02a..68e7e9f1 100644
--- a/doc/cpp/morphology.rst
+++ b/doc/cpp/morphology.rst
@@ -77,6 +77,11 @@ consistent parent-child indexing, and with ``n`` segments numbered from ``0`` to
Join two subtrees at a given id, such that said id becomes the parent
of the inserted sub-tree.
+.. cpp:function:: std::vector<msize_t> tag_roots(const segment_tree& t, int tag)
+
+ Get IDs of roots of a region with specific tag in the segment tree, i.e. segments whose
+ parent is either :data:`mnpos` or a segment with a different tag.
+
.. cpp:function:: bool equivalent(const segment_tree& l, const segment_tree& r)
Two trees are equivalent if
diff --git a/doc/dev/simd_api.rst b/doc/dev/simd_api.rst
index 1b315a61..a8236f03 100644
--- a/doc/dev/simd_api.rst
+++ b/doc/dev/simd_api.rst
@@ -1060,7 +1060,7 @@ wrap calls to the Intel scalar vector mathematics library (SVML).
Outside of this case, the functions *exp*, *log*, *expm1* and
*exprelr* use explicit approximations as detailed below. The
-algortihms follow those used in the
+algorithms follow those used in the
`Cephes library <http://www.netlib.org/cephes/>`_, with
some accommodations.
diff --git a/doc/python/morphology.rst b/doc/python/morphology.rst
index f0ee2045..ae00ea1c 100644
--- a/doc/python/morphology.rst
+++ b/doc/python/morphology.rst
@@ -254,6 +254,12 @@ Cable cell morphology
``join_at`` is inverse to ``split_at`` for a proper choice of ``id``.
The join point ``id`` must be in ``L``.
+ .. method:: tag_roots(tag)
+
+ Get IDs of roots of region with a particular ``tag`` in the segment tree, i.e.
+ segments whose parent is either :data:`mnpos` or a segment with a different
+ tag.
+
.. method:: apply_isometry(iso)
Apply an :py:class:`isometry` to the segment tree, returns the transformed tree as a copy.
diff --git a/doc/tutorial/index.rst b/doc/tutorial/index.rst
index 0e54ec31..629e81c9 100644
--- a/doc/tutorial/index.rst
+++ b/doc/tutorial/index.rst
@@ -24,6 +24,7 @@ Cells
single_cell_detailed
single_cell_cable
single_cell_allen
+ single_cell_bluepyopt
Recipes
-------
diff --git a/doc/tutorial/network_ring_gpu.rst b/doc/tutorial/network_ring_gpu.rst
index 834a6fb8..fe3966a4 100644
--- a/doc/tutorial/network_ring_gpu.rst
+++ b/doc/tutorial/network_ring_gpu.rst
@@ -69,7 +69,7 @@ hardware Arbor will execute on.
Step **(14)** creates a :class:`arbor.partition_hint`, and tells it to put 1000 cells in a groups allocated to GPUs,
and to prefer the utilisation of the GPU if present. In fact, the default distribution strategy of
:class:`arbor.partition_load_balance` already spreads out cells as evenly as possible over CPUs, and groups
-(up to 1000) on GPUs, so strictly speaking it was not necesary to give that part of the hint.
+(up to 1000) on GPUs, so strictly speaking it was not necessary to give that part of the hint.
Lastly, a dictionary is created with which hints are assigned to a particular :class:`arbor.cell_kind`.
Different kinds may favor different execution, hence the option.
In this simulation, there are only :class:`arbor.cell_kind.cable`, so we assign the hint to that kind.
diff --git a/doc/tutorial/network_ring_mpi.rst b/doc/tutorial/network_ring_mpi.rst
index 08a01b44..d7031185 100644
--- a/doc/tutorial/network_ring_mpi.rst
+++ b/doc/tutorial/network_ring_mpi.rst
@@ -75,7 +75,7 @@ The results
Before we execute the simulation, we have to understand how Arbor distributes the computational load over the ranks.
After executing ``mpirun``, all nodes will run the same script. In the domain decomposition step, the nodes will use
-the provided MPI communicator to divide the work. Once :py:func:`arbor.simulation.run` starts, each node wil work on
+the provided MPI communicator to divide the work. Once :py:func:`arbor.simulation.run` starts, each node will work on
their allocated cell ``gid`` s.
This is relevant for the collection of results: these are not gathered for you. Remember that in step **(15)** we
diff --git a/doc/tutorial/probe_lfpykit.rst b/doc/tutorial/probe_lfpykit.rst
index ac2a076f..3faa6e86 100644
--- a/doc/tutorial/probe_lfpykit.rst
+++ b/doc/tutorial/probe_lfpykit.rst
@@ -18,7 +18,7 @@ These are signals that mainly stem from transmembrane currents.
1. Recording of transmembrane currents using :class:`arbor.cable_probe_total_current_cell`
2. Recording of stimulus currents using :class:`arbor.cable_probe_stimulus_current_cell`
3. Using the :class:`arbor.place_pwlin` API
- 4. Map recorded transmembrane currents to extracellular potentials by derivinging Arbor specific classes from LFPykit's :class:`lfpykit.LineSourcePotential` and :class:`lfpykit.CellGeometry`
+ 4. Map recorded transmembrane currents to extracellular potentials by deriving Arbor specific classes from LFPykit's :class:`lfpykit.LineSourcePotential` and :class:`lfpykit.CellGeometry`
.. _tutorial_lfpykit-linesource:
diff --git a/doc/tutorial/single_cell_bluepyopt.rst b/doc/tutorial/single_cell_bluepyopt.rst
new file mode 100644
index 00000000..6f7db0bb
--- /dev/null
+++ b/doc/tutorial/single_cell_bluepyopt.rst
@@ -0,0 +1,305 @@
+.. _tutorialsinglecellbluepyopt:
+
+A single cell model from the BluePyOpt Cell Optimisation Library
+================================================================
+
+In this tutorial we'll see how we can export a model with optimised cell parameters from the `BluePyOpt package <https://github.com/BlueBrain/BluePyOpt>`_. In the process, we will learn to edit the morphology in order to replace the axon with a surrogate model and use the Arbor GUI to define stimuli and probe locations that are consistent with BluePyOpt protocols for the Neuron simulator.
+
+.. Note::
+
+ **Concepts covered in this example:**
+
+ 1. Export a model with optimised parameters from BluePyOpt to a mixed JSON/ACC format.
+ 2. Load the morphology, label dictionary and decor from the mixed JSON/ACC format in Arbor.
+ 3. Perform axon replacement with a surrogate model using the segment tree editing functionality.
+ 4. Determine voltage probe locations that match BluePyOpt protocols defined with the Neuron simulator using the Arbor graphical user interface (GUI).
+ 5. Create an :class:`arbor.cable_cell` and an :class:`arbor.single_cell_model` or :class:`arbor.recipe` supporting mechanism catalogues that are consistent with BluePyOpt.
+ 6. Running a simulation and visualising the results.
+
+The model
+---------
+
+We will focus on the neocortical layer-5 pyramidal cell from the BluePyOpt repository documented in the `L5PC Jupyter notebook <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb>`_ and the references listed thereunder, in particular the `BluePyOpt paper (2015) in Frontiers of Neuroinformatics <https://www.frontiersin.org/articles/10.3389/fninf.2016.00017/full>`_. The steps shown in the following can also be performed on BluePyOpt's `simple-cell example <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb>`_ for a more basic setup.
+
+The layer-5 pyramidal cell model is specified with the `morphology <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc/morphology>`_ in ASC-format, the `mechanisms <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc/mechanisms>`_, which match Arbor's Blue Brain Project catalogue, and their mapping to morphological regions in `mechanisms.json <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/config/mechanisms.json>`_ as well as mechanism parameters in `parameters.json <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/config/parameters.json>`_. The model exclusively uses density mechanisms. However, models with point-processes can also be exported to the JSON/ACC format presented in this tutorial.
+
+Export to JSON/ACC
+------------------
+
+Before we can export a model, we first have to create it inside BluePyOpt. This can be achieved from the `l5pc directory <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc>`_ by running
+
+.. code-block:: python
+
+ from bluepyopt import ephys
+ import l5pc_model
+
+ cell_model = ephys.models.CellModel(
+ 'l5pc',
+ morph=ephys.morphologies.NrnFileMorphology('morphology/C060114A7.asc',
+ do_replace_axon=False),
+ mechs=l5pc_model.define_mechanisms(),
+ params=l5pc_model.define_parameters())
+
+For the optimised parameters, we will use those published by Markram et al. "Reconstruction and simulation of neocortical microcircuitry." Cell 163.2 (2015): 456-492, as cited in the `L5PC notebook <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb>`_.
+
+.. code-block:: python
+
+ param_values = {
+ "gNaTs2_tbar_NaTs2_t.apical": 0.026145,
+ "gSKv3_1bar_SKv3_1.apical": 0.004226,
+ "gImbar_Im.apical": 0.000143,
+ "gNaTa_tbar_NaTa_t.axonal": 3.137968,
+ "gK_Tstbar_K_Tst.axonal": 0.089259,
+ "gamma_CaDynamics_E2.axonal": 0.002910,
+ "gNap_Et2bar_Nap_Et2.axonal": 0.006827,
+ "gSK_E2bar_SK_E2.axonal": 0.007104,
+ "gCa_HVAbar_Ca_HVA.axonal": 0.000990,
+ "gK_Pstbar_K_Pst.axonal": 0.973538,
+ "gSKv3_1bar_SKv3_1.axonal": 1.021945,
+ "decay_CaDynamics_E2.axonal": 287.198731,
+ "gCa_LVAstbar_Ca_LVAst.axonal": 0.008752,
+ "gamma_CaDynamics_E2.somatic": 0.000609,
+ "gSKv3_1bar_SKv3_1.somatic": 0.303472,
+ "gSK_E2bar_SK_E2.somatic": 0.008407,
+ "gCa_HVAbar_Ca_HVA.somatic": 0.000994,
+ "gNaTs2_tbar_NaTs2_t.somatic": 0.983955,
+ "decay_CaDynamics_E2.somatic": 210.485284,
+ "gCa_LVAstbar_Ca_LVAst.somatic": 0.000333
+ }
+
+We can now export the model to a given target directory using the ``create_acc`` module in BluePyOpt.
+
+.. code-block:: python
+
+ ephys.create_acc.output_acc('path/to/exported/l5pc', cell_model, param_values)
+
+This exports a model in a mixed JSON/ACC format analogous to that in the example directory of Arbor at ``python/example/single_cell_bluepyopt/l5pc``. The whole set of steps to construct and export the model is automated in the script `generate_acc.py <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/generate_acc.py>`_, part of BluePyOpt.
+
+This script also supports axon replacement by first instantiating the morphology in BluePyOpt through the Neuron simulator before exporting the model to JSON/ACC with:
+
+.. code-block:: python
+
+ nrn_sim = ephys.simulators.NrnSimulator()
+ cell_model.instantiate_morphology_3d(nrn_sim)
+
+The model in the example directory ``python/example/single_cell_bluepyopt/l5pc`` was exported using this setting. For this reason, we find an axon-replacement morphology and a modified morphology (after performing axon replacement on the original) in the ACC format in that directory. The latter can be obtained by setting ``create_mod_morph=True`` as a parameter to ``create_acc``. For a more basic example, consider ``python/example/single_cell_bluepyopt/simplecell``, where no axon replacement is performed and, hence, only the original morphology is exported.
+
+Load from JSON/ACC
+------------------
+
+The exported model is summarised by a JSON file in the output directory, that references the original morphology file (for the layer-5 pyramical cell in ASC, for the simple-cell in SWC), a label dictionary and decor (both in ACC) with relative paths. For the simple-cell example, this amounts to
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt/simplecell/simple_cell.json
+ :language: json
+ :dedent:
+
+In case of axon replacement, the morphology to replace the axon with (``replace_axon``) and (optionally) the morphology resulting after performing axon-replacement on the original (``modified``) are also exported in the ACC format, as in the layer-5 pyramidal cell example.
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt/l5pc/l5pc.json
+ :language: json
+ :dedent:
+ :lines: 4-8
+
+We use a file-format specific loader for the morphology (e.g. :py:func:`~arbor.load_asc`), while the label-dict and decor can be loaded as Arbor cable cell components (:py:func:`~arbor.load_component`). So, for the exported ``l5pc`` example in the Arbor repository, we can set:
+
+.. code-block:: python
+
+ cell_json_filename = 'python/example/single_cell_bluepyopt/l5pc/l5pc.json'
+
+and use
+
+.. code-block:: python
+
+ with open(cell_json_filename) as f:
+ cell_json = json.load(f)
+ morpho = arbor.load_asc(cell_json['morphology']['original'])
+ labels = arbor.load_component(cell_json['label_dict']).component
+ decor = arbor.load_component(cell_json['decor']).component
+
+This loads the original morphology. For the exported simple-cell, this represents the morphology used inside BluePyOpt. On the other hand, for the exported layer-5 pyramidal cell the modifications in terms of axon replacement still have to be applied. We will turn to that topic in the following.
+
+In BluePyOpt, the above steps can be abbreviated using the ``read_acc`` function. This also reconstructs the morphology in a way that is faithful to BluePyOpt's Neuron-based axon replacement procedure.
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt_l5pc.py
+ :language: python
+ :dedent:
+ :lines: 20
+
+Axon replacement
+----------------
+
+A common modeling choice for cell optimisation with the Neuron simulator in BluePyOpt is to substitute the axon by a simpler surrogate morphology that is computationally cheaper to simulate. `The assumption is that the axon is often not modeled well or at all, and its function is poorly understood <https://www.nature.com/articles/s41467-017-02718-3>`_.
+
+The corresponding composite morphology can be obtained in Arbor by substituting the axon of the original morphology (``original``) with the exported replacement (``replace_axon``) using the segment tree editing primitives :func:`arbor.segment_tree.split_at` and :func:`arbor.segment_tree.join_at`. The axon-replacement morphology is automatically exported to ACC when calling ``create_acc`` on a cell model with a morphology that has ``do_replace_axon`` set to ``True``. If the axon-replacement exported by BluePyOpt is not satisfactory, alternatively, the user can also define his/her own morphology in ACC to use as a replacement.
+
+To perform axon replacement, we first have to find the (unique) root segment of this region in the morphology. We can then split the segment tree at the axon root into two separate segment trees - a pruned one and one for the original axon.
+
+.. code-block:: python
+
+ # get unique axon root (tag for axon is 2)
+ axon_root = morpho.segment_tree.tag_roots(2)[0]
+
+ # prune morphology at axon root
+ pruned_st, axon_st = morpho.segment_tree.split_at(axon_root)
+
+Then we can determine the parent of the axon root to join the replacement onto, which we load from ACC. Alternatively, the user can build/supply a custom replacement here.
+
+.. code-block:: python
+
+ # parent segment to join onto
+ axon_parent = morpho.segment_tree.parents[axon_root]
+
+ # load replacement morphology
+ axon_replacement = arbor.load_component(
+ cell_json['morphology']['replace_axon']).component
+ axon_replacement_st = replacement.to_segment_tree()
+
+ # pruned segment tree with axon replacement
+ joined_st = pruned_st.join_at(
+ axon_parent, axon_replacement_st)
+
+ # final morphology
+ morpho_axon_replaced = arbor.morphology(joined_st)
+
+The steps presented here are performed automatically when loading an exported model with the above ``read_acc`` function from BluePyOpt.
+
+
+Define locations in the GUI
+---------------------------
+
+BluePyOpt makes use of the Neuron simulator's features to define custom locations for stimuli and voltage probes that can be challenging to translate to Arbor. As an example, the layer-5 pyramidal cell optimisation problem has a protocol named ``bAP`` with multiple voltage probes located distally on the apical dendrite at a fixed distance from the soma as described in `config/protocols.json <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/config/protocols.json>`_.
+
+To describe these locations accurately in Arbor, we will use the Arbor GUI. For this purpose, we create an :class:`arbor.cable_cell` and export it to ACC so that it can be loaded in the GUI,
+
+.. code-block:: python
+
+ cell = arbor.cable_cell(morpho, decor, labels)
+ arbor.write_component(cell, 'path/to/l5pc_cable_cell.acc')
+
+After loading this file in the GUI with ``File > Cable cell > Load``, we can click the ``Locations`` and ``Cell`` tab to obtain the following overview.
+
+.. figure:: single_cell_bluepyopt_gui_l5pc_overview.png
+ :width: 800
+ :align: center
+ :class: with-border
+
+ Apical view on the layer-5 pyramidal cell morphology with axon-replacement performed. The replacement axon is not visible from this perspective as hidden by the soma (red). Colour legend for regions on the left.
+
+The first extra recording of the ``bAP`` protocol is located at 660 µm from the soma. As a first attempt to formulate this location using Arbor's S-expression syntax for :ref:`regions <labels-region-expr>` and :ref:`locsets <labels-locset-expr>`, we can use
+
+.. code-block::
+
+ (distal-translate (on-components 0.5 (region "soma")) 660)
+
+and since we're only interested in the apical dendrite, we can wrap this in ``(restrict ... (region "apic"))``.
+
+Unfortunately, for this particular morphology, this does not result in a concrete location as can be seen in the Arbor GUI below (Locset A, violet, is empty). Alternatively, the method :func:`arbor.cable_cell.locations` will also return an empty list of concrete locations for this expression. To obtain a valid location instead, we can replace ``(on-components 0.5 (region "soma"))`` by the proximal boundary of the apical dendrite with ``(proximal (region "apic"))`` (Locset B, orange). The restrict operation to the apical region is no longer necessary. But now, the resulting concrete location is non-unique.
+
+.. figure:: single_cell_bluepyopt_gui_l5pc_bAP_attempt.png
+ :width: 800
+ :align: center
+ :class: with-border
+
+ First two attempts (Locset A and B) to define a location for the extra recording in the ``bAP`` protocol on the layer-5 pyramidal cell morphology with axon-replacement.
+
+BluePyOpt solves this issue with the Neuron simulator by choosing a particular section on the apical dendrite. While a similar approach is possible in Arbor using the :func:`arbor.cable_cell.locations` method, for reproducibility it is preferable to determine the location directly oneself as a user in the Arbor GUI.
+
+For this purpose, we can identify a particular branch (or segment) by hovering over the distal apical part of the morphology with the mouse and keeping a focus on the data in the left lower corner. We can then define a region with
+
+.. code-block::
+
+ (proximal-interval (distal (branch <branch-id>)))
+
+that includes the path (Region C, yellow) from that branch to the (apical) root (Locset D, blue). Restricting the above ``distal-translate``-set of locations to this region evaluates to a unique concrete location for the probe (Locset F, red).
+
+.. figure:: single_cell_bluepyopt_gui_l5pc_bAP_final.png
+ :width: 800
+ :align: center
+ :class: with-border
+
+ Proximal region of a distal apical branch (Region C, between Locset D and E) and locset at fixed distance from the apical root for the extra recording in the ``bAP`` protocol on the layer-5 pyramidal cell morphology with axon-replacement.
+
+The final expression for the probe location on the morphology with axon-replacement, hence, is
+
+.. code-block::
+
+ (restrict
+ (distal-translate
+ (proximal (region "apic"))
+ 660)
+ (proximal-interval
+ (distal (branch 123))))
+
+For the following, we will add a label for this location as well as for the center of the soma to the label dictionary.
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt_l5pc.py
+ :language: python
+ :dedent:
+ :lines: 13,24-28
+
+
+The cable cell and protocols
+----------------------------
+
+Following BluePyOpt's layer-5 pyramidal cell example, we have to define protocols with stimuli and probes as well as make the appropriate mechanism catalogues available to the recipe.
+
+To replicate the ``bAP`` protocol in BluePyOpt, we add a short stimulus centrally at the soma to the decor.
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt_l5pc.py
+ :language: python
+ :dedent:
+ :lines: 30,32-34,38-40
+
+The decor allows to further customise the discretisation for a subsequent simulation using :func:`arbor.decor.discretisation` as done in the :ref:`tutorial for a detailed single cell model <tutorialsinglecellswc>`. For brevity, we will use the default here except on the soma. As a next step, we create an :class:`arbor.cable_cell` using the morphology, label dictionary and decor.
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt_l5pc.py
+ :language: python
+ :dedent:
+ :lines: 42,44
+
+Then, we can install a voltage probe on the apical dendrite.
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt_l5pc.py
+ :language: python
+ :dedent:
+ :lines: 46,48
+
+
+To prepare the cable cell for simulation, we can either construct an instance of the :class:`arbor.single_cell_model` from the cable cell (shown in the simple-cell example) or - for more complicated use cases - define our own recipe. We will follow the latter for the layer-5 pyramidal cell.
+
+To be runnable in a simulation, we also need to make sure the appropriate mechanism catalogues are loaded. For the layer-5 pyramidal cell example, all used mechanisms are built into arbor's :func:`arbor.bbp_catalogue`. For the simple-cell example, they are built into the :func:`arbor.default_catalogue`. Therefore, the following lines defining the recipe's global properties will suffice to run these examples. Note that we are using explicit prefix qualifiers - this is required by BluePyOpt's JSON/ACC exporter.
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt_l5pc.py
+ :language: python
+ :dedent:
+ :lines: 51-92
+
+If we want to run a model with mechanism catalogues that are not built into Arbor, we can specify a mapping of catalogue name to folder with the NMODL files in the argument ``ext_catalogues`` of the JSON/ACC exporter. We can then load the catalogue with an appropriate prefix analogous to the built-in ones using ``arbor.load_catalogue('<name>-catalogue.so', '<name>::')`` and use it with the exported model. Here, ``<name>-catalogue.so`` is the compiled output of ``arbor-build-catalogue <name> <path/to/nmodl>``. To use such a mechanism catalogue in a BluePyOpt optimisation, ``<name>-catalogue.so`` must be available in the same folder as the NMODL files at runtime. It is then sufficient, to supply the same ``ext_catalogues`` dictionary as used in the JSON/ACC exporter to the ``ephys.simulators.ArbSimulator`` at its construction.
+
+Running the simulation
+----------------------
+
+We can now run a simulation with
+
+.. literalinclude:: ../../python/example/single_cell_bluepyopt_l5pc.py
+ :language: python
+ :dedent:
+ :lines: 94-96,100-104
+
+and visualise the voltage trace for the measurement on the apical dendrite of the layer-5 pyramidal cell that is available through ``sim.samples(handle)``.
+
+.. figure:: single_cell_bluepyopt_l5pc_bAP_dend1.svg
+ :width: 600
+ :align: center
+
+
+For the simple-cell, we obtain the following voltage trace using the ``step2`` protocol from the `Jupyter notebook <https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb>`_
+
+.. figure:: single_cell_bluepyopt_simplecell.svg
+ :width: 600
+ :align: center
+
+The full code
+-------------
+
+You can find the source code for this example in full at ``python/examples/single_cell_bluepyopt_l5pc.py`` and ``python/examples/single_cell_bluepyopt_simplecell.py``.
diff --git a/doc/tutorial/single_cell_bluepyopt_gui_l5pc_bAP_attempt.png b/doc/tutorial/single_cell_bluepyopt_gui_l5pc_bAP_attempt.png
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diff --git a/python/cable_cell_io.cpp b/python/cable_cell_io.cpp
index 335371df..61d8d9f0 100644
--- a/python/cable_cell_io.cpp
+++ b/python/cable_cell_io.cpp
@@ -1,5 +1,6 @@
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
+#include <pybind11/iostream.h>
#include <fstream>
#include <iostream>
@@ -30,13 +31,25 @@ arborio::cable_cell_component load_component(py::object fn) {
template<typename T>
void write_component(const T& component, py::object fn) {
- std::ofstream fid(util::to_path(fn));
- arborio::write_component(fid, component, arborio::meta_data{});
+ if (py::hasattr(fn, "write")) {
+ std::ostringstream fid;
+ py::scoped_ostream_redirect redirect{fid, fn};
+ arborio::write_component(fid, component, arborio::meta_data{});
+ } else {
+ std::ofstream fid(util::to_path(fn));
+ arborio::write_component(fid, component, arborio::meta_data{});
+ }
}
void write_component(const arborio::cable_cell_component& component, py::object fn) {
- std::ofstream fid(util::to_path(fn));
- arborio::write_component(fid, component);
+ if (py::hasattr(fn, "write")) {
+ std::ostringstream fid;
+ py::scoped_ostream_redirect redirect{fid, fn};
+ arborio::write_component(fid, component);
+ } else {
+ std::ofstream fid(util::to_path(fn));
+ arborio::write_component(fid, component);
+ }
}
void register_cable_loader(pybind11::module& m) {
diff --git a/python/example/single_cell_bluepyopt/l5pc/C060114A7.asc b/python/example/single_cell_bluepyopt/l5pc/C060114A7.asc
new file mode 100644
index 00000000..ba898076
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+ |
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+
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+
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+ ) ; End of markers
+ Normal
+ |
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+ ) ; End of markers
+ Normal
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+ ) ; End of markers
+ Normal
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+
+ (Cross
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+ ) ; End of markers
+ Normal
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+ |
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+
+ (Cross
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+ ) ; End of markers
+ Normal
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+
+( (Color Green)
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+ (Cross
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+ ( 140.90 8.84 24.00 0.46) ; 29
+ ( 140.90 8.84 23.98 0.46) ; 30
+ ( 139.12 8.42 25.13 0.46) ; 31
+ ( 136.30 8.36 25.70 0.46) ; 32
+ ( 136.30 8.36 25.73 0.46) ; 33
+
+ (Cross
+ (Color Green)
+ (Name "Marker 3")
+ ( 183.62 7.50 10.70 0.46) ; 1
+ ( 185.28 8.49 10.70 0.46) ; 2
+ ( 174.57 11.95 14.90 0.46) ; 3
+ ( 165.19 9.75 18.48 0.46) ; 4
+ ( 168.01 9.82 18.48 0.46) ; 5
+ ( 160.15 9.17 19.27 0.46) ; 6
+ ( 161.18 14.79 21.00 0.46) ; 7
+ ( 157.78 9.22 21.25 0.46) ; 8
+ ( 150.45 6.30 21.25 0.46) ; 9
+ ( 151.52 7.75 21.25 0.46) ; 10
+ ( 154.21 8.38 21.25 0.46) ; 11
+ ( 155.86 9.36 21.25 0.46) ; 12
+ ( 148.21 5.78 21.25 0.46) ; 13
+ ( 143.18 11.16 25.73 0.46) ; 14
+ ( 141.11 5.91 25.75 0.46) ; 15
+ ( 141.08 10.08 24.88 0.46) ; 16
+ ) ; End of markers
+ Normal
+ |
+ ( 198.48 10.40 6.07 0.92) ; 1, R-1-1-2
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+ ( 190.00 8.41 -0.32 0.92) ; 5
+ ( 188.21 7.99 -0.95 0.46) ; 6
+ ( 186.12 6.90 -0.95 0.46) ; 7
+ ( 183.17 7.41 -1.60 0.46) ; 8
+ ( 183.17 7.41 -1.63 0.46) ; 9
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+ ( 182.09 5.96 -5.80 0.46) ; 11
+ ( 179.47 7.13 -7.50 0.46) ; 12
+ ( 179.47 7.13 -7.53 0.46) ; 13
+ ( 177.23 6.61 -8.13 0.46) ; 14
+ ( 174.55 5.98 -8.93 0.46) ; 15
+ ( 172.69 7.93 -10.45 0.46) ; 16
+ ( 172.69 7.93 -10.50 0.46) ; 17
+ ( 170.95 9.31 -11.27 0.46) ; 18
+ ( 170.55 11.01 -12.60 0.46) ; 19
+ ( 170.55 11.01 -12.67 0.46) ; 20
+ ( 171.67 14.26 -14.18 0.46) ; 21
+ ( 169.49 15.53 -15.60 0.46) ; 22
+ ( 166.73 17.28 -16.80 0.46) ; 23
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+ ( 163.82 19.59 -19.75 0.46) ; 25
+ ( 162.71 22.31 -20.35 0.46) ; 26
+ ( 162.71 22.31 -20.38 0.46) ; 27
+ ( 161.43 23.80 -22.27 0.46) ; 28
+ ( 159.69 25.19 -23.60 0.46) ; 29
+ ( 158.08 26.01 -25.50 0.46) ; 30
+ ( 158.08 26.01 -25.55 0.46) ; 31
+ ( 155.59 26.62 -26.92 0.46) ; 32
+ ( 153.40 27.89 -28.70 0.46) ; 33
+ ( 151.16 27.37 -30.90 0.46) ; 34
+ ( 150.76 29.07 -32.80 0.46) ; 35
+ ( 150.19 29.53 -35.40 0.46) ; 36
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+ ( 148.32 31.48 -41.40 0.46) ; 40
+ ( 147.88 31.38 -43.78 0.46) ; 41
+ ( 147.88 31.38 -43.80 0.46) ; 42
+
+ (Cross
+ (Color Green)
+ (Name "Marker 3")
+ ( 198.67 11.63 4.07 0.92) ; 1
+ ( 196.31 11.67 2.15 0.92) ; 2
+ ( 184.91 6.01 -1.63 0.46) ; 3
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+ ( 176.60 5.27 -7.90 0.46) ; 5
+ ( 175.63 7.42 -7.32 0.46) ; 6
+ ( 175.36 8.57 -9.05 0.46) ; 7
+ ( 174.63 3.61 -10.95 0.46) ; 8
+ ( 170.14 6.73 -12.42 0.46) ; 9
+ ( 169.47 9.56 -9.77 0.46) ; 10
+ ( 169.57 13.17 -14.18 0.46) ; 11
+ ( 168.06 17.59 -15.02 0.46) ; 12
+ ( 173.09 12.20 -15.02 0.46) ; 13
+ ( 173.14 14.01 -15.60 0.46) ; 14
+ ( 166.81 14.91 -15.60 0.46) ; 15
+ ( 169.44 13.73 -15.60 0.46) ; 16
+ ( 159.80 18.64 -20.38 0.46) ; 17
+ ( 164.64 22.16 -21.45 0.46) ; 18
+ ( 163.92 23.20 -22.27 0.46) ; 19
+ ( 156.12 24.35 -27.08 0.46) ; 20
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
+ ( 237.57 0.45 -4.55 1.38) ; 1, R-1-2
+ (
+ ( 234.22 2.64 -6.25 0.92) ; 1, R-1-2-1
+ ( 234.22 2.64 -6.28 0.92) ; 2
+ ( 231.91 4.49 -7.55 0.92) ; 3
+ ( 231.91 4.49 -7.57 0.92) ; 4
+ ( 229.99 4.64 -8.93 0.92) ; 5
+ ( 228.03 2.98 -10.17 0.92) ; 6
+ ( 225.21 2.92 -9.88 0.92) ; 7
+ ( 221.11 4.35 -11.37 0.92) ; 8
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+ ( 219.82 5.83 -14.25 0.92) ; 11
+ ( 219.82 5.83 -14.27 0.92) ; 12
+ ( 217.82 8.36 -13.82 0.92) ; 13
+ ( 216.52 9.85 -15.57 0.92) ; 14
+ ( 215.50 10.20 -17.67 0.92) ; 15
+ ( 215.50 10.20 -17.70 0.92) ; 16
+ ( 213.26 9.68 -19.33 0.92) ; 17
+ ( 211.61 8.69 -19.60 0.92) ; 18
+ ( 210.85 7.91 -21.10 0.92) ; 19
+ ( 210.85 7.91 -21.13 0.92) ; 20
+ ( 209.65 7.03 -22.92 0.46) ; 21
+ ( 209.11 9.30 -24.90 0.46) ; 22
+ ( 208.86 10.43 -26.90 0.92) ; 23
+ ( 205.14 10.16 -28.13 0.92) ; 24
+ ( 205.14 10.16 -28.15 0.92) ; 25
+ ( 203.09 10.88 -28.15 0.92) ; 26
+ ( 199.88 12.51 -28.15 0.92) ; 27
+
+ (Cross
+ (Color Green)
+ (Name "Marker 3")
+ ( 224.58 1.58 -9.88 0.92) ; 1
+ ( 216.74 6.91 -13.82 0.92) ; 2
+ ( 214.37 6.95 -19.33 0.92) ; 3
+ ( 213.44 10.91 -19.33 0.92) ; 4
+ ( 211.70 6.33 -19.60 0.92) ; 5
+ ( 202.11 13.03 -28.15 0.92) ; 6
+ ( 205.18 11.96 -27.65 0.92) ; 7
+ ) ; End of markers
+ (
+ ( 195.01 13.16 -27.70 0.46) ; 1, R-1-2-1-1
+ ( 191.49 14.13 -29.27 0.46) ; 2
+ ( 190.32 15.05 -30.30 0.46) ; 3
+ ( 188.33 17.57 -30.83 0.46) ; 4
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+ ( 184.77 22.70 -33.22 0.46) ; 6
+ ( 183.61 23.63 -34.52 0.46) ; 7
+ ( 183.61 23.63 -34.55 0.46) ; 8
+ ( 182.37 26.92 -34.55 0.46) ; 9
+ ( 179.97 31.13 -34.60 0.46) ; 10
+ ( 179.97 31.13 -34.63 0.46) ; 11
+ ( 177.84 34.22 -37.02 0.46) ; 12
+ ( 177.84 34.22 -37.05 0.46) ; 13
+ ( 175.64 35.50 -38.03 0.46) ; 14
+ ( 174.40 38.79 -38.38 0.46) ; 15
+ ( 171.19 40.42 -38.60 0.46) ; 16
+ ( 169.64 43.04 -39.67 0.46) ; 17
+ ( 167.59 43.76 -40.57 0.46) ; 18
+ ( 167.59 43.76 -40.60 0.46) ; 19
+
+ (Cross
+ (Color Green)
+ (Name "Marker 3")
+ ( 183.61 23.63 -33.22 0.46) ; 1
+ ( 183.17 29.49 -38.35 0.46) ; 2
+ ) ; End of markers
+ Normal
+ |
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+ ( 185.74 14.56 -37.88 0.46) ; 11
+ ( 182.21 15.54 -39.13 0.46) ; 12
+ ( 178.94 15.37 -40.80 0.46) ; 13
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+ ( 168.56 19.50 -50.35 0.46) ; 20
+ ( 168.56 19.50 -50.38 0.46) ; 21
+ ( 167.71 21.10 -52.65 0.46) ; 22
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+ ( 167.45 22.22 -54.65 0.46) ; 24
+ ( 167.45 22.22 -54.72 0.46) ; 25
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+ ( 163.61 22.52 -58.40 0.46) ; 29
+ ( 165.22 21.71 -60.05 0.46) ; 30
+ ( 165.22 21.71 -60.22 0.46) ; 31
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+ ( 162.84 21.75 -62.75 0.46) ; 33
+ ( 161.33 20.20 -66.13 0.46) ; 34
+ ( 159.99 19.89 -69.00 0.46) ; 35
+ ( 158.52 20.14 -69.30 0.46) ; 36
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+ ( 154.72 38.36 -86.65 0.46) ; 50
+ ( 153.88 39.95 -89.05 0.46) ; 51
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+ ( 151.61 43.59 -94.80 0.46) ; 53
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+ ( 149.73 45.55 -96.80 0.46) ; 55
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+ ( 147.60 48.62 -102.63 0.46) ; 57
+ ( 147.60 48.62 -102.65 0.46) ; 58
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 147.72 48.06 -50.55 0.46) ; 1
+ ) ; End of markers
+
+ (Cross
+ (Color Green)
+ (Name "Marker 3")
+ ( 193.45 9.81 -32.30 0.46) ; 1
+ ( 190.54 12.12 -31.77 0.46) ; 2
+ ( 183.19 13.37 -39.13 0.46) ; 3
+ ( 179.21 14.24 -41.85 0.46) ; 4
+ ( 168.74 20.74 -48.38 0.46) ; 5
+ ( 158.79 19.00 -67.32 0.46) ; 6
+ ( 157.53 38.41 -86.65 0.46) ; 7
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
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+ ( 232.98 -0.04 -7.43 0.92) ; 2
+ ( 231.72 -2.72 -8.00 0.92) ; 3
+ ( 231.72 -2.72 -8.02 0.92) ; 4
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+ ( 167.63 -75.08 -67.17 0.46) ; 60
+ ( 167.63 -75.08 -67.28 0.46) ; 61
+
+ (Cross
+ (Color Green)
+ (Name "Marker 3")
+ ( 225.37 -7.79 -8.50 0.92) ; 1
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+ ( 226.89 -6.25 -8.50 0.92) ; 3
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+ ( 217.64 -31.10 -7.95 0.92) ; 10
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+ ( 177.46 -72.78 -42.02 0.46) ; 20
+ ( 175.40 -78.04 -43.80 0.46) ; 21
+ ) ; End of markers
+ Normal
+ |
+ ( 236.33 -2.03 -4.55 0.92) ; 1, R-1-2-3
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+ ( 233.37 -7.50 -0.40 0.92) ; 6
+ (
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+ ( 237.37 -18.51 6.57 0.92) ; 8
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+ ( 235.59 -35.06 12.50 0.92) ; 17
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+ ( 234.20 -37.16 12.05 0.92) ; 19
+ ( 233.75 -37.27 14.30 0.92) ; 20
+ ( 234.65 -37.06 16.57 0.92) ; 21
+ ( 235.36 -38.09 18.67 0.92) ; 22
+ ( 233.83 -39.65 20.17 0.92) ; 23
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+ ( 232.35 -45.37 21.88 0.92) ; 25
+ ( 230.33 -48.83 21.55 0.92) ; 26
+ ( 230.33 -48.83 21.52 0.92) ; 27
+ ( 229.79 -52.53 22.08 0.92) ; 28
+ ( 227.64 -55.43 22.57 0.92) ; 29
+ ( 226.57 -56.87 23.33 0.92) ; 30
+ ( 224.92 -57.85 23.80 0.92) ; 31
+ ( 224.92 -57.85 23.78 0.92) ; 32
+ ( 224.11 -60.44 24.38 0.92) ; 33
+ ( 224.11 -60.44 24.35 0.92) ; 34
+ ( 225.53 -62.48 25.23 0.92) ; 35
+ ( 223.64 -66.51 26.45 0.92) ; 36
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+ ( 217.55 -78.69 31.95 0.92) ; 41
+ ( 216.47 -80.14 33.85 0.92) ; 42
+ ( 216.02 -80.24 34.40 0.92) ; 43
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 239.02 -17.53 5.85 0.92) ; 1
+ ( 233.48 -20.03 9.20 0.92) ; 2
+ ( 235.13 -19.04 9.20 0.92) ; 3
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+ ( 226.20 -59.35 23.07 0.92) ; 17
+ ( 221.99 -67.50 26.45 0.92) ; 18
+ ( 219.80 -72.19 27.30 0.92) ; 19
+ ( 225.59 -70.83 27.30 0.92) ; 20
+ ( 224.21 -72.95 28.52 0.92) ; 21
+ ) ; End of markers
+ High
+ |
+ ( 231.22 -10.40 -1.42 0.92) ; 1, R-1-2-3-2
+ ( 231.89 -13.23 -2.42 0.92) ; 2
+ ( 231.89 -13.23 -2.45 0.92) ; 3
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+ ( 220.34 -46.38 6.07 0.92) ; 16
+ ( 220.34 -46.38 5.22 0.92) ; 17
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+ ( 203.59 -151.83 13.30 0.92) ; 59
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 216.18 -68.86 -1.60 0.92) ; 22
+ ( 210.20 -93.55 -3.78 0.92) ; 23
+ ( 212.42 -99.00 -1.77 0.92) ; 24
+ ( 207.40 -103.76 -4.70 0.92) ; 25
+ ( 211.24 -104.05 -5.57 0.92) ; 26
+ ( 211.18 -111.83 -2.22 0.92) ; 27
+ ( 208.49 -118.44 -1.32 0.92) ; 28
+ ( 211.61 -117.71 -1.32 0.92) ; 29
+ ( 211.88 -118.83 -1.32 0.92) ; 30
+ ( 202.84 -130.51 4.60 0.92) ; 31
+ ( 198.85 -135.62 4.60 0.92) ; 32
+ ( 207.07 -132.50 4.17 0.92) ; 33
+ ( 203.60 -145.85 9.52 0.92) ; 34
+ ( 203.24 -148.33 9.52 0.92) ; 35
+ ( 213.10 -128.10 3.32 0.92) ; 36
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ ) ; End of split
+ ) ; End of split
+ |
+ ( 250.10 9.55 4.75 0.92) ; 1, R-2
+ ( 247.61 10.16 6.35 0.92) ; 2
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+ ( 244.26 12.36 14.88 0.92) ; 8
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 246.60 16.50 8.85 0.92) ; 2
+ ( 248.46 14.54 11.47 0.92) ; 3
+ ) ; End of markers
+ (
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+ ( 241.54 15.91 14.45 0.92) ; 2
+ ( 240.08 16.16 14.85 0.92) ; 3
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+ ( 239.86 19.09 16.60 0.92) ; 5
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+ ( 231.76 31.53 20.13 0.92) ; 10
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 237.23 20.27 18.40 0.92) ; 2
+ ( 240.13 23.93 18.40 0.92) ; 3
+ ( 238.44 27.12 19.02 0.92) ; 4
+ ( 231.53 28.49 18.55 0.92) ; 5
+ ( 234.26 30.92 18.55 0.92) ; 6
+ ( 232.58 34.11 18.55 0.92) ; 7
+ ) ; End of markers
+ (
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+ ( 227.31 36.46 21.85 0.46) ; 2
+ ( 224.86 38.86 22.67 0.46) ; 3
+ ( 223.84 39.22 24.20 0.46) ; 4
+ ( 223.39 39.12 24.20 0.46) ; 5
+ ( 222.63 38.35 26.35 0.46) ; 6
+ Normal
+ |
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+ ( 226.23 35.01 19.58 0.92) ; 2
+ ( 226.23 35.01 19.55 0.92) ; 3
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+ ( 217.41 42.50 25.60 0.92) ; 7
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 214.29 41.77 25.05 0.92) ; 1
+ ( 218.09 45.64 25.05 0.92) ; 2
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
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+ ( 242.29 10.71 16.38 0.92) ; 2
+ ( 242.29 10.71 16.35 0.92) ; 3
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+ ( 227.29 8.38 26.60 0.92) ; 12
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+ ( 221.62 6.46 33.70 0.46) ; 20
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 219.30 14.28 30.67 0.92) ; 7
+ ( 229.42 11.07 -25.80 0.92) ; 8
+ ) ; End of markers
+ High
+ |
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+ ( 247.29 9.49 18.80 0.46) ; 2
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+ ( 244.06 5.15 24.80 0.46) ; 5
+ ( 243.18 4.94 24.80 0.46) ; 6
+ (
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+ ( 247.13 -1.89 30.57 0.46) ; 6
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+ ( 248.46 -1.57 33.27 0.46) ; 8
+
+ (Cross
+ (Color Green)
+ (Name "Marker 3")
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+ ( 244.86 1.76 26.52 0.46) ; 3
+ ( 242.19 1.13 26.52 0.46) ; 4
+ ( 242.72 -1.14 26.52 0.46) ; 5
+ ) ; End of markers
+ High
+ |
+ ( 246.70 3.98 24.80 0.46) ; 1, R-2-3-2
+ ( 248.17 3.73 23.27 0.46) ; 2
+ ( 248.57 2.03 21.77 0.46) ; 3
+ (
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+ ( 266.39 -11.71 32.30 0.46) ; 12
+
+ (Cross
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+ ( 259.11 -12.82 23.95 0.46) ; 4
+ ( 264.53 -9.75 26.65 0.46) ; 5
+ ( 264.43 -13.36 27.10 0.46) ; 6
+ ) ; End of markers
+ High
+ |
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+ ( 142.90 -43.64 -3.95 0.46) ; 47
+
+ (Cross
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+ (Name "Marker 3")
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+ ( 166.33 -25.01 7.25 0.46) ; 28
+ ( 164.23 -26.10 7.25 0.46) ; 29
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+ ( 161.24 -27.40 5.63 0.46) ; 31
+ ( 161.27 -31.57 5.60 0.46) ; 32
+ ) ; End of markers
+ Incomplete
+ ) ; End of split
+ ) ; End of split
+ ) ; End of split
+ ) ; End of split
+) ; End of tree
+
+( (Color Yellow)
+ (Dendrite)
+ ( 273.70 23.33 0.80 1.38) ; Root
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+ ( 282.42 28.35 -2.08 1.38) ; 7
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 274.62 25.34 2.42 0.46) ; 1
+ ) ; End of markers
+ (
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+ ( 371.05 112.44 -1.60 0.46) ; 59
+ ( 371.05 112.44 -1.63 0.46) ; 60
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 355.57 92.09 -16.38 0.92) ; 16
+ ( 366.32 112.52 -5.65 0.46) ; 17
+ ( 363.90 104.78 -9.67 0.46) ; 18
+ ) ; End of markers
+ Normal
+ |
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+ (Cross
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+ Normal
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+( (Color Yellow)
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+ Normal
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+ ( 301.01 27.34 -116.70 0.46) ; 33
+ ( 300.75 28.46 -120.52 0.46) ; 34
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 293.82 23.86 -97.78 0.46) ; 1
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
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+
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+
+ (Cross
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+ Normal
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+ Normal
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+ ) ; End of markers
+ Normal
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+ ) ; End of markers
+ Normal
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+
+( (Color Cyan)
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+ ) ; End of markers
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+ Normal
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+ Normal
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+ ) ; End of markers
+ High
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+ ) ; End of markers
+ Normal
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+ |
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+
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+ ) ; End of markers
+ Normal
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+ |
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+
+ (Cross
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+ Normal
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+ ( 261.98 151.23 17.85 0.92) ; 6
+ ( 262.55 144.80 15.48 0.92) ; 7
+ ( 256.08 152.24 18.95 0.92) ; 8
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
+ ( 262.76 141.86 4.85 0.92) ; 1, R-1-1-1-1-1-2-2
+ ( 263.97 142.74 3.60 0.92) ; 2
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+ ( 324.23 249.43 -45.25 0.46) ; 60
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 281.52 184.47 -12.88 0.92) ; 18
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+ ( 261.43 141.55 6.00 0.92) ; 29
+ ( 274.95 160.24 21.07 0.92) ; 30
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ ) ; End of split
+ |
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+ ( 305.80 201.54 -106.35 0.46) ; 83
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 285.22 122.59 7.53 0.46) ; 19
+ ( 290.42 130.44 -34.47 0.92) ; 20
+ ( 289.74 127.28 -34.95 0.92) ; 21
+ ( 285.36 123.87 -31.63 0.92) ; 22
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
+ ( 255.15 73.83 0.70 0.92) ; 1, R-1-1-1-2
+ ( 252.75 72.06 0.70 0.92) ; 2
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+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 255.35 75.06 0.70 0.92) ; 1
+ ( 254.72 73.72 7.57 0.92) ; 2
+ ( 249.44 70.09 3.78 0.92) ; 3
+ ( 249.18 71.22 3.78 0.92) ; 4
+ ( 248.51 74.05 8.02 0.92) ; 5
+ ) ; End of markers
+ (
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+ ( 251.87 77.83 6.42 1.38) ; 2
+ ( 252.05 79.06 8.70 1.38) ; 3
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+ ( 255.94 80.57 15.65 1.38) ; 9
+ ( 259.51 81.40 16.80 1.38) ; 10
+ ( 263.22 81.68 17.95 1.38) ; 11
+ ( 265.35 78.59 19.15 1.38) ; 12
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 262.42 85.07 16.80 1.38) ; 1
+ ( 257.32 82.69 16.83 1.38) ; 2
+ ( 255.31 79.22 15.65 1.38) ; 3
+ ) ; End of markers
+ (
+ ( 263.70 77.61 19.75 1.38) ; 1, R-1-1-1-2-1-1
+ ( 263.70 77.61 19.73 1.38) ; 2
+ ( 260.62 78.69 21.45 0.92) ; 3
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+ ( 258.31 86.51 28.88 0.92) ; 8
+ ( 257.60 87.53 30.23 0.92) ; 9
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 261.47 77.09 21.45 0.92) ; 1
+ ( 259.15 78.94 24.40 0.92) ; 2
+ ) ; End of markers
+ High
+ |
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+ ( 281.28 76.95 25.87 0.92) ; 10
+ ( 281.28 76.95 25.85 0.92) ; 11
+ ( 281.86 76.49 28.10 0.92) ; 12
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 266.42 80.05 19.15 1.38) ; 1
+ ( 268.33 73.92 21.75 0.92) ; 2
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
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+ ( 183.94 60.10 29.42 0.92) ; 39
+ ( 183.54 61.81 30.60 0.92) ; 40
+ ( 183.54 61.81 30.57 0.92) ; 41
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 241.78 76.66 8.35 0.92) ; 2
+ ( 238.26 77.62 8.07 0.92) ; 3
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+ ( 227.17 72.64 13.02 0.92) ; 5
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+ ( 234.71 72.62 11.35 0.92) ; 9
+ ( 218.01 67.50 14.60 0.92) ; 10
+ ( 213.77 69.49 12.60 0.92) ; 11
+ ( 212.39 67.39 15.20 0.92) ; 12
+ ( 201.84 60.13 18.45 0.92) ; 13
+ ( 201.21 58.78 16.20 0.92) ; 14
+ ( 198.58 59.97 16.80 0.92) ; 15
+ ( 184.36 54.24 24.65 0.92) ; 16
+ ( 192.22 54.90 24.27 0.92) ; 17
+ ( 187.89 47.30 25.20 0.92) ; 18
+ ( 198.88 54.65 18.45 0.92) ; 19
+ ) ; End of markers
+ High
+ ) ; End of split
+ |
+ ( 259.28 74.90 3.67 0.92) ; 1, R-1-1-1-3
+ ( 259.28 74.90 3.65 0.92) ; 2
+ ( 260.35 76.34 5.97 0.92) ; 3
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+ ( 266.44 88.52 11.00 0.46) ; 10
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+ ( 268.28 90.74 12.65 0.46) ; 12
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+ ( 273.59 90.19 15.88 0.46) ; 15
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+ ( 279.68 96.40 19.60 0.46) ; 19
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+ ( 289.16 108.18 24.05 0.46) ; 28
+ ( 290.23 109.63 23.17 0.46) ; 29
+ ( 289.83 111.32 22.45 0.46) ; 30
+ ( 289.83 111.32 22.43 0.46) ; 31
+ ( 288.99 112.91 20.55 0.46) ; 32
+ ( 288.42 113.38 18.50 0.46) ; 33
+ ( 288.42 113.38 18.48 0.46) ; 34
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
+ ( 289.62 111.72 -21.60 0.92) ; 1
+ ( 286.18 96.74 23.45 0.46) ; 2
+ ( 267.12 91.67 12.65 0.46) ; 3
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+ ( 271.01 93.18 14.75 0.46) ; 6
+ ( 276.18 93.20 17.95 0.46) ; 7
+ ( 277.16 91.04 19.50 0.46) ; 8
+ ( 282.00 94.56 22.30 0.46) ; 9
+ ( 277.52 93.52 22.32 0.46) ; 10
+ ( 279.10 96.87 18.45 0.46) ; 11
+ ( 283.64 95.54 23.45 0.46) ; 12
+ ( 265.27 80.96 19.15 1.38) ; 13
+ ( 266.64 79.62 8.77 0.46) ; 14
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
+ ( 251.51 49.77 -3.38 0.92) ; 1, R-1-1-2
+ ( 249.56 54.10 -2.95 0.92) ; 2
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+ ( 207.11 54.30 -15.50 0.92) ; 21
+ ( 204.16 54.80 -17.13 0.92) ; 22
+ ( 201.61 55.31 -16.57 0.92) ; 23
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 208.27 53.37 -15.50 0.92) ; 10
+ ) ; End of markers
+ (
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+ ( 167.65 55.80 1.05 0.46) ; 19
+ ( 166.94 56.82 2.45 0.46) ; 20
+ ( 166.94 56.82 2.42 0.46) ; 21
+ ( 166.36 57.29 4.32 0.46) ; 22
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 198.35 55.14 -22.05 0.92) ; 3
+ ( 176.16 47.64 -3.47 0.92) ; 4
+ ) ; End of markers
+ Normal
+ |
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+ ( 170.71 50.46 -2.95 0.46) ; 18
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+ ( 168.75 54.77 -1.20 0.46) ; 20
+ ( 167.47 56.27 0.00 0.46) ; 21
+ ( 166.44 56.63 1.63 0.46) ; 22
+ ( 166.44 56.63 1.60 0.46) ; 23
+ ( 165.42 56.98 2.90 0.46) ; 24
+
+ (Cross
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+ (Name "Marker 3")
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+ ( 179.40 43.54 -4.70 0.46) ; 5
+ ( 198.85 55.35 -16.57 0.92) ; 6
+ ( 177.44 47.85 -4.70 0.46) ; 7
+ ) ; End of markers
+ Normal
+ |
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+ ( 91.10 60.46 -106.15 0.46) ; 67
+ ( 88.74 60.51 -109.20 0.46) ; 68
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 153.46 41.63 -44.07 0.46) ; 7
+ ( 147.97 40.95 -50.55 0.46) ; 8
+ ( 165.13 46.16 -32.55 0.92) ; 9
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ ) ; End of split
+ |
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+
+ (Cross
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+ ( 290.33 63.05 -13.07 0.92) ; 8
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+ ( 297.75 85.69 -30.45 0.46) ; 26
+ ( 299.54 86.12 -31.92 0.46) ; 27
+ ( 300.75 86.99 -33.80 0.46) ; 28
+
+ (Cross
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+ ( 279.64 56.37 -13.55 0.92) ; 10
+ ( 280.86 57.24 -13.55 0.92) ; 11
+ ) ; End of markers
+ (
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+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 313.19 88.13 -39.10 0.46) ; 2
+ ) ; End of markers
+ Normal
+ |
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+ ( 314.02 156.99 -59.83 0.46) ; 39
+ ( 317.96 160.29 -59.15 0.46) ; 40
+ ( 319.13 165.34 -58.05 0.46) ; 41
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+ ( 317.71 173.37 -60.07 0.46) ; 43
+ ( 317.05 176.20 -61.42 0.46) ; 44
+ ( 315.63 178.26 -64.27 0.46) ; 45
+
+ (Cross
+ (Color White)
+ (Name "Marker 3")
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+ ( 316.51 172.50 -60.07 0.46) ; 2
+ ( 305.00 135.16 -58.75 0.46) ; 3
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+ ( 302.18 90.91 -39.10 0.46) ; 10
+ ( 301.95 87.87 -33.53 0.46) ; 11
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ |
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+
+ (Cross
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+ ( 290.28 51.10 -20.00 0.92) ; 2
+ ) ; End of markers
+ (
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+ ( 303.79 85.91 -92.15 0.46) ; 48
+ ( 303.66 86.48 -95.30 0.46) ; 49
+
+ (Cross
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+ (Name "Marker 3")
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+ ( 295.04 62.97 -51.25 0.46) ; 4
+ ( 291.81 58.62 -36.52 0.46) ; 5
+ ( 293.99 51.37 -21.82 0.92) ; 6
+ ) ; End of markers
+ Normal
+ |
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+ ( 339.80 -47.75 -52.88 0.46) ; 41
+ ( 340.28 -51.82 -52.70 0.46) ; 42
+
+ (Cross
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+ ( 311.54 32.79 -35.45 0.46) ; 2
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+ ( 338.33 -47.50 -52.70 0.46) ; 10
+ ) ; End of markers
+ Normal
+ ) ; End of split
+ ) ; End of split
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diff --git a/python/example/single_cell_bluepyopt/l5pc/C060114A7_axon_replacement.acc b/python/example/single_cell_bluepyopt/l5pc/C060114A7_axon_replacement.acc
new file mode 100644
index 00000000..9318d731
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/l5pc/C060114A7_axon_replacement.acc
@@ -0,0 +1,21 @@
+(arbor-component
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\ No newline at end of file
diff --git a/python/example/single_cell_bluepyopt/l5pc/C060114A7_modified.acc b/python/example/single_cell_bluepyopt/l5pc/C060114A7_modified.acc
new file mode 100644
index 00000000..0f0910d5
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/l5pc/C060114A7_modified.acc
@@ -0,0 +1,21702 @@
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diff --git a/python/example/single_cell_bluepyopt/l5pc/Readme.md b/python/example/single_cell_bluepyopt/l5pc/Readme.md
new file mode 100644
index 00000000..adbd49ac
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/l5pc/Readme.md
@@ -0,0 +1,13 @@
+# BluePyOpt layer-5 pyramidal cell with axon-replacement
+
+This example was created in an environment with the `bluepyopt` package installed (see [installation instructions](https://github.com/BlueBrain/BluePyOpt#installation)). A cell model with parameters as published by [Markram et al., "Reconstruction and simulation of neocortical microcircuitry", Cell 163.2 (2015): 456–492](http://www.cell.com/abstract/S0092-8674%2815%2901191-5) (see [L5PC.ipynb](https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb)) can be exported with
+
+```shell
+python /path/to/BluePyOpt/examples/l5pc/generate_acc.py --output <output-dir> --replace-axon
+```
+
+We use the Arbor BBP mechanism catalogue as a substitute for the one in BluePyOpt and an Arbor simulation using this cell model can be launched using
+
+```shell
+python ../../single_cell_bluepyopt_l5pc.py l5pc.json
+```
diff --git a/python/example/single_cell_bluepyopt/l5pc/l5pc.json b/python/example/single_cell_bluepyopt/l5pc/l5pc.json
new file mode 100644
index 00000000..a8a9fefc
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/l5pc/l5pc.json
@@ -0,0 +1,11 @@
+{
+ "cell_model_name": "l5pc",
+ "produced_by": "Created by BluePyOpt(1.12.100) at 2022-10-12 21:53:49.177709",
+ "morphology": {
+ "original": "C060114A7.asc",
+ "replace_axon": "C060114A7_axon_replacement.acc",
+ "modified": "C060114A7_modified.acc"
+ },
+ "label_dict": "l5pc_label_dict.acc",
+ "decor": "l5pc_decor.acc"
+}
\ No newline at end of file
diff --git a/python/example/single_cell_bluepyopt/l5pc/l5pc_decor.acc b/python/example/single_cell_bluepyopt/l5pc/l5pc_decor.acc
new file mode 100644
index 00000000..89c7735b
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/l5pc/l5pc_decor.acc
@@ -0,0 +1,37 @@
+(arbor-component
+ (meta-data (version "0.1-dev"))
+ (decor
+ (default (membrane-potential -65))
+ (default (temperature-kelvin 307.14999999999998))
+ (default (membrane-capacitance 0.01))
+ (default (axial-resistivity 100))
+ (paint (region "all") (density (mechanism "default::pas/e=-75" ("g" 3.0000000000000001e-05))))
+ (paint (region "apic") (ion-reversal-potential "na" 50))
+ (paint (region "apic") (ion-reversal-potential "k" -85))
+ (paint (region "apic") (membrane-capacitance 0.02))
+ (paint (region "apic") (density (mechanism "BBP::NaTs2_t" ("gNaTs2_tbar" 0.026145000000000002))))
+ (paint (region "apic") (density (mechanism "BBP::SKv3_1" ("gSKv3_1bar" 0.0042259999999999997))))
+ (paint (region "apic") (density (mechanism "BBP::Im" ("gImbar" 0.00014300000000000001))))
+ (paint (region "apic") (scaled-mechanism (density (mechanism "BBP::Ih" ("gIhbar" 8.0000000000000007e-05))) ("gIhbar" (add (scalar -0.86960000000000004) (mul (scalar 2.0870000000000002) (exp (mul (distance (region "soma")) (scalar 0.0030999999999999999) ) ) ) ))))
+ (paint (region "axon") (ion-reversal-potential "na" 50))
+ (paint (region "axon") (ion-reversal-potential "k" -85))
+ (paint (region "axon") (density (mechanism "BBP::NaTa_t" ("gNaTa_tbar" 3.1379679999999999))))
+ (paint (region "axon") (density (mechanism "BBP::Nap_Et2" ("gNap_Et2bar" 0.0068269999999999997))))
+ (paint (region "axon") (density (mechanism "BBP::K_Pst" ("gK_Pstbar" 0.97353800000000001))))
+ (paint (region "axon") (density (mechanism "BBP::K_Tst" ("gK_Tstbar" 0.089259000000000005))))
+ (paint (region "axon") (density (mechanism "BBP::SK_E2" ("gSK_E2bar" 0.0071040000000000001))))
+ (paint (region "axon") (density (mechanism "BBP::SKv3_1" ("gSKv3_1bar" 1.0219450000000001))))
+ (paint (region "axon") (density (mechanism "BBP::Ca_HVA" ("gCa_HVAbar" 0.00098999999999999999))))
+ (paint (region "axon") (density (mechanism "BBP::Ca_LVAst" ("gCa_LVAstbar" 0.0087519999999999994))))
+ (paint (region "axon") (density (mechanism "BBP::CaDynamics_E2" ("gamma" 0.0029099999999999998) ("decay" 287.19873100000001))))
+ (paint (region "dend") (membrane-capacitance 0.02))
+ (paint (region "dend") (density (mechanism "BBP::Ih" ("gIhbar" 8.0000000000000007e-05))))
+ (paint (region "soma") (ion-reversal-potential "na" 50))
+ (paint (region "soma") (ion-reversal-potential "k" -85))
+ (paint (region "soma") (density (mechanism "BBP::NaTs2_t" ("gNaTs2_tbar" 0.98395500000000002))))
+ (paint (region "soma") (density (mechanism "BBP::SKv3_1" ("gSKv3_1bar" 0.30347200000000002))))
+ (paint (region "soma") (density (mechanism "BBP::SK_E2" ("gSK_E2bar" 0.0084069999999999995))))
+ (paint (region "soma") (density (mechanism "BBP::Ca_HVA" ("gCa_HVAbar" 0.00099400000000000009))))
+ (paint (region "soma") (density (mechanism "BBP::Ca_LVAst" ("gCa_LVAstbar" 0.00033300000000000002))))
+ (paint (region "soma") (density (mechanism "BBP::CaDynamics_E2" ("gamma" 0.00060899999999999995) ("decay" 210.48528400000001))))
+ (paint (region "soma") (density (mechanism "BBP::Ih" ("gIhbar" 8.0000000000000007e-05))))))
\ No newline at end of file
diff --git a/python/example/single_cell_bluepyopt/l5pc/l5pc_label_dict.acc b/python/example/single_cell_bluepyopt/l5pc/l5pc_label_dict.acc
new file mode 100644
index 00000000..08c4efd5
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/l5pc/l5pc_label_dict.acc
@@ -0,0 +1,9 @@
+(arbor-component
+ (meta-data (version "0.1-dev"))
+ (label-dict
+ (region-def "all" (all))
+ (region-def "apic" (tag 4))
+ (region-def "axon" (tag 2))
+ (region-def "dend" (tag 3))
+ (region-def "soma" (tag 1))
+ (region-def "myelin" (tag 5))))
\ No newline at end of file
diff --git a/python/example/single_cell_bluepyopt/simplecell/Readme.md b/python/example/single_cell_bluepyopt/simplecell/Readme.md
new file mode 100644
index 00000000..c8c9a981
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/simplecell/Readme.md
@@ -0,0 +1,14 @@
+# BluePyOpt simple cell output
+
+This example was created in an environment with the `bluepyopt` package installed (see [installation instructions](https://github.com/BlueBrain/BluePyOpt#installation)). A cell model with parameters optimized in [simplecell.ipynb](https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb) can be exported with
+
+
+```shell
+python /path/to/BluePyOpt/examples/simplecell/generate_acc.py --output <output-dir>
+```
+
+An Arbor simulation using this cell model can then be launched using
+
+```shell
+python ../../single_cell_bluepyopt_simple.py simple_cell.json
+```
diff --git a/python/example/single_cell_bluepyopt/simplecell/simple.swc b/python/example/single_cell_bluepyopt/simplecell/simple.swc
new file mode 100644
index 00000000..cf106230
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/simplecell/simple.swc
@@ -0,0 +1,4 @@
+# Dummy granule cell morphology
+1 1 -5.0 0.0 0.0 5.0 -1
+2 1 0.0 0.0 0.0 5.0 1
+3 1 5.0 0.0 0.0 5.0 2
diff --git a/python/example/single_cell_bluepyopt/simplecell/simple_cell.json b/python/example/single_cell_bluepyopt/simplecell/simple_cell.json
new file mode 100644
index 00000000..e62bb454
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/simplecell/simple_cell.json
@@ -0,0 +1,9 @@
+{
+ "cell_model_name": "simple_cell",
+ "produced_by": "Created by BluePyOpt(1.12.100) at 2022-10-12 21:36:47.495284",
+ "morphology": {
+ "original": "simple.swc"
+ },
+ "label_dict": "simple_cell_label_dict.acc",
+ "decor": "simple_cell_decor.acc"
+}
\ No newline at end of file
diff --git a/python/example/single_cell_bluepyopt/simplecell/simple_cell_decor.acc b/python/example/single_cell_bluepyopt/simplecell/simple_cell_decor.acc
new file mode 100644
index 00000000..e5af159c
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/simplecell/simple_cell_decor.acc
@@ -0,0 +1,5 @@
+(arbor-component
+ (meta-data (version "0.1-dev"))
+ (decor
+ (paint (region "soma") (membrane-capacitance 0.01))
+ (paint (region "soma") (density (mechanism "default::hh" ("gnabar" 0.10299326453483033) ("gkbar" 0.027124836082684685))))))
\ No newline at end of file
diff --git a/python/example/single_cell_bluepyopt/simplecell/simple_cell_label_dict.acc b/python/example/single_cell_bluepyopt/simplecell/simple_cell_label_dict.acc
new file mode 100644
index 00000000..08c4efd5
--- /dev/null
+++ b/python/example/single_cell_bluepyopt/simplecell/simple_cell_label_dict.acc
@@ -0,0 +1,9 @@
+(arbor-component
+ (meta-data (version "0.1-dev"))
+ (label-dict
+ (region-def "all" (all))
+ (region-def "apic" (tag 4))
+ (region-def "axon" (tag 2))
+ (region-def "dend" (tag 3))
+ (region-def "soma" (tag 1))
+ (region-def "myelin" (tag 5))))
\ No newline at end of file
diff --git a/python/example/single_cell_bluepyopt_l5pc.py b/python/example/single_cell_bluepyopt_l5pc.py
new file mode 100755
index 00000000..6af3d21a
--- /dev/null
+++ b/python/example/single_cell_bluepyopt_l5pc.py
@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+
+import arbor
+import pandas
+import seaborn
+import sys
+
+try:
+ from bluepyopt import ephys
+except ImportError:
+ raise ImportError("Please install bluepyopt to run this example.")
+
+# (1) Read the cell JSON description referencing morphology, label dictionary and decor.
+
+if len(sys.argv) < 2:
+ print("No JSON file passed to the program")
+ sys.exit(0)
+
+cell_json_filename = sys.argv[1]
+cell_json, morpho, decor, labels = ephys.create_acc.read_acc(cell_json_filename)
+
+# (2) Define labels for stimuli and voltage recordings.
+
+labels["soma_center"] = "(location 0 0.5)"
+labels["dend1"] = (
+ '(restrict (distal-translate (proximal (region "apic")) 660)'
+ " (proximal-interval (distal (branch 123))))"
+)
+
+# (3) Define stimulus and spike detector, adjust discretization
+
+decor.place(
+ '"soma_center"', arbor.iclamp(tstart=295, duration=5, current=1.9), "soma_iclamp"
+)
+
+# Add spike detector
+decor.place('"soma_center"', arbor.threshold_detector(-10), "detector")
+
+# Adjust discretization (single CV on soma, default everywhere else)
+decor.discretization(arbor.cv_policy_max_extent(1.0) | arbor.cv_policy_single('"soma"'))
+
+# (4) Create the cell.
+
+cell = arbor.cable_cell(morpho, decor, labels)
+
+# (5) Declare a probe.
+
+probe = arbor.cable_probe_membrane_voltage('"dend1"')
+
+
+# (6) Create a class that inherits from arbor.recipe
+class single_recipe(arbor.recipe):
+
+ # (6.1) 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_props = arbor.neuron_cable_properties()
+
+ # Add catalogues with explicit qualifiers
+ self.the_props.catalogue = arbor.catalogue()
+ self.the_props.catalogue.extend(arbor.default_catalogue(), "default::")
+ self.the_props.catalogue.extend(arbor.bbp_catalogue(), "BBP::")
+
+ # (6.2) Override the num_cells method
+ def num_cells(self):
+ return 1
+
+ # (6.3) Override the num_targets method
+ def cell_kind(self, gid):
+ return arbor.cell_kind.cable
+
+ # (6.4) Override the cell_description method
+ def cell_description(self, gid):
+ return self.the_cell
+
+ # (6.5) Override the probes method
+ def probes(self, gid):
+ return self.the_probes
+
+ # (6.6) Overrode the global_properties method
+ def global_properties(self, gid):
+ return self.the_props
+
+
+# Instantiate recipe
+# Pass the probe in a list because that it what single_recipe expects.
+recipe = single_recipe(cell, [probe])
+
+# (7) Create a simulation (using defaults for context and partition_load_balance)
+sim = arbor.simulation(recipe)
+
+# 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))
+
+# (8) Run the simulation
+sim.run(tfinal=600, dt=0.025)
+
+# (9) 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_list = []
+for i in range(len(data)):
+ df_list.append(
+ pandas.DataFrame(
+ {
+ "t/ms": data[i][:, 0],
+ "U/mV": data[i][:, 1],
+ "Location": str(meta[i]),
+ "Variable": "voltage",
+ }
+ )
+ )
+df = pandas.concat(df_list, ignore_index=True)
+seaborn.relplot(
+ data=df, kind="line", x="t/ms", y="U/mV", hue="Location", col="Variable", ci=None
+).savefig("single_cell_bluepyopt_l5pc_bAP_dend1.svg")
diff --git a/python/example/single_cell_bluepyopt_simplecell.py b/python/example/single_cell_bluepyopt_simplecell.py
new file mode 100755
index 00000000..1b413164
--- /dev/null
+++ b/python/example/single_cell_bluepyopt_simplecell.py
@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+
+import arbor
+import pandas
+import seaborn
+import sys
+
+try:
+ from bluepyopt import ephys
+except ImportError:
+ raise ImportError("Please install bluepyopt to run this example.")
+
+# (1) Read the cell JSON description referencing morphology, label dictionary and decor.
+
+if len(sys.argv) < 2:
+ print("No JSON file passed to the program")
+ sys.exit(0)
+
+cell_json_filename = sys.argv[1]
+cell_json, morpho, decor, labels = ephys.create_acc.read_acc(cell_json_filename)
+
+# (2) Define labels for stimuli and voltage recordings.
+
+labels["soma_center"] = "(location 0 0.5)"
+
+# (3) Define stimulus and spike detector, adjust discretization
+
+decor.place(
+ '"soma_center"', arbor.iclamp(tstart=100, duration=50, current=0.05), "soma_iclamp"
+)
+
+# Add spike detector
+decor.place('"soma_center"', arbor.threshold_detector(-10), "detector")
+
+# Adjust discretization (single CV on soma, default everywhere else)
+decor.discretization(arbor.cv_policy_max_extent(1.0) | arbor.cv_policy_single('"soma"'))
+
+# (4) Create the cell.
+
+cell = arbor.cable_cell(morpho, decor, labels)
+
+# (5) Make the single cell model.
+
+m = arbor.single_cell_model(cell)
+
+# Add catalogues with qualifiers
+m.properties.catalogue = arbor.catalogue()
+m.properties.catalogue.extend(arbor.default_catalogue(), "default::")
+m.properties.catalogue.extend(arbor.bbp_catalogue(), "BBP::")
+
+# (6) Attach voltage probe that samples at 50 kHz.
+m.probe("voltage", where='"soma_center"', frequency=50)
+
+# (7) Simulate the cell for 200 ms.
+m.run(tfinal=200)
+print("Simulation done.")
+
+# (8) Print spike times.
+if len(m.spikes) > 0:
+ print("{} spikes:".format(len(m.spikes)))
+ for s in m.spikes:
+ print(" {:7.4f}".format(s))
+else:
+ print("no spikes")
+
+# (9) Plot the recorded voltages over time.
+print("Plotting results ...")
+df_list = []
+for t in m.traces:
+ df_list.append(
+ pandas.DataFrame(
+ {
+ "t/ms": t.time,
+ "U/mV": t.value,
+ "Location": str(t.location),
+ "Variable": t.variable,
+ }
+ )
+ )
+
+df = pandas.concat(df_list, ignore_index=True)
+
+seaborn.relplot(
+ data=df, kind="line", x="t/ms", y="U/mV", hue="Location", col="Variable", ci=None
+).savefig("single_cell_bluepyopt_simplecell.svg")
diff --git a/python/morphology.cpp b/python/morphology.cpp
index d1ca3446..b0ba4367 100644
--- a/python/morphology.cpp
+++ b/python/morphology.cpp
@@ -237,6 +237,12 @@ void register_morphology(py::module& m) {
},
"parent"_a, "x"_a, "y"_a, "z"_a, "radius"_a, "tag"_a,
"Append a segment to the tree, using the distal location of the parent segment as the proximal end.")
+ .def("is_fork", &arb::segment_tree::is_fork,
+ "i"_a, "True if segment has more than one child.")
+ .def("is_terminal", &arb::segment_tree::is_terminal,
+ "i"_a, "True if segment has no children.")
+ .def("is_root", &arb::segment_tree::is_root,
+ "i"_a, "True if segment has no parent.")
// properties
.def_property_readonly("empty", [](const arb::segment_tree& st){return st.empty();},
"Indicates whether the tree is empty (i.e. whether it has size 0)")
@@ -258,6 +264,9 @@ void register_morphology(py::module& m) {
.def("equivalent",
[](const arb::segment_tree& t, const arb::segment_tree& o) { return arb::equivalent(t, o); },
"Two trees are equivalent, but not neccessarily identical, ie they have the same segments and structure.")
+ .def("tag_roots",
+ [](const arb::segment_tree& t, int tag) { return arb::tag_roots(t, tag); },
+ "Get roots of tag region of this segment tree.")
.def("__str__", [](const arb::segment_tree& s) {
return util::pprintf("<arbor.segment_tree:\n{}>", s);});
@@ -310,6 +319,7 @@ void register_morphology(py::module& m) {
"See the documentation https://docs.arbor-sim.org/en/latest/fileformat/swc.html\n"
"for a detailed description of the interpretation.");
+
// arb::morphology
py::class_<arb::morphology> morph(m, "morphology");
@@ -336,6 +346,8 @@ void register_morphology(py::module& m) {
return m.branch_segments(i);
},
"i"_a, "A list of the segments in branch i, ordered from proximal to distal ends of the branch.")
+ .def("to_segment_tree", &arb::morphology::to_segment_tree,
+ "Convert this morphology to a segment_tree.")
.def("__str__",
[](const arb::morphology& m) {
return util::pprintf("<arbor.morphology:\n{}>", m);
diff --git a/python/single_cell_model.cpp b/python/single_cell_model.cpp
index cc8c0cb6..486b0307 100644
--- a/python/single_cell_model.cpp
+++ b/python/single_cell_model.cpp
@@ -15,6 +15,7 @@
#include <arbor/simulation.hpp>
#include <arbor/util/any_cast.hpp>
+#include "event_generator.hpp"
#include "error.hpp"
#include "strprintf.hpp"
@@ -74,12 +75,14 @@ struct single_cell_recipe: arb::recipe {
const arb::cable_cell& cell_;
const std::vector<probe_site>& probes_;
const arb::cable_cell_global_properties& gprop_;
+ const std::vector<arb::event_generator>& event_generators_;
single_cell_recipe(
const arb::cable_cell& c,
const std::vector<probe_site>& probes,
- const arb::cable_cell_global_properties& props):
- cell_(c), probes_(probes), gprop_(props)
+ const arb::cable_cell_global_properties& props,
+ const std::vector<arb::event_generator>& event_generators):
+ cell_(c), probes_(probes), gprop_(props), event_generators_(event_generators)
{}
virtual arb::cell_size_type num_cells() const override {
@@ -90,18 +93,18 @@ struct single_cell_recipe: arb::recipe {
return cell_;
}
- virtual arb::cell_kind get_cell_kind(arb::cell_gid_type) const override {
+ virtual arb::cell_kind get_cell_kind(arb::cell_gid_type gid) const override {
return arb::cell_kind::cable;
}
// connections and event generators
- virtual std::vector<arb::cell_connection> connections_on(arb::cell_gid_type) const override {
- return {}; // no connections on a single cell model
+ virtual std::vector<arb::cell_connection> connections_on(arb::cell_gid_type gid) const override {
+ return {};
}
virtual std::vector<arb::event_generator> event_generators(arb::cell_gid_type) const override {
- return {};
+ return event_generators_;
}
// probes
@@ -121,7 +124,7 @@ struct single_cell_recipe: arb::recipe {
return {}; // No gap junctions on a single cell model.
}
- virtual std::any get_global_properties(arb::cell_kind) const override {
+ virtual std::any get_global_properties(arb::cell_kind kind) const override {
return gprop_;
}
};
@@ -132,6 +135,7 @@ class single_cell_model {
bool run_ = false;
std::vector<probe_site> probes_;
+ std::vector<arb::event_generator> event_generators_;
std::unique_ptr<arb::simulation> sim_;
std::vector<double> spike_times_;
// Create one trace for each probe.
@@ -166,8 +170,13 @@ public:
}
}
+ void event_generator(const arb::event_generator& event_generator) {
+ event_generators_.push_back(event_generator);
+ }
+
void run(double tfinal, double dt) {
- single_cell_recipe rec(cell_, probes_, gprop);
+ // single_cell_recipe rec(cell_, probes_, gprop, spike_sources_, connections_);
+ single_cell_recipe rec(cell_, probes_, gprop, event_generators_);
auto domdec = arb::partition_load_balance(rec, ctx_);
@@ -201,6 +210,10 @@ public:
run_ = true;
}
+ const arb::cable_cell& cable_cell() const {
+ return cell_;
+ }
+
const std::vector<double>& spike_times() const {
return spike_times_;
}
@@ -248,6 +261,16 @@ void register_single_cell(pybind11::module& m) {
" what: Name of the variable to record (currently only 'voltage').\n"
" where: Location on cell morphology at which to sample the variable.\n"
" frequency: The target frequency at which to sample [kHz].")
+ .def("event_generator",
+ [](single_cell_model& m, const pyarb::event_generator_shim& event_generator) {
+ m.event_generator(arb::event_generator(
+ event_generator.target, event_generator.weight, event_generator.time_sched));},
+ "event_generator"_a,
+ "Register an event generator.\n"
+ " event_generator: An Arbor event generator.")
+ .def_property_readonly("cable_cell",
+ [](const single_cell_model& m) {
+ return m.cable_cell();}, "The cable cell held by this model.")
.def_property_readonly("spikes",
[](const single_cell_model& m) {
return m.spike_times();}, "Holds spike times [ms] after a call to run().")
diff --git a/test/unit/test_segment_tree.cpp b/test/unit/test_segment_tree.cpp
index 7875db33..f7c16b6d 100644
--- a/test/unit/test_segment_tree.cpp
+++ b/test/unit/test_segment_tree.cpp
@@ -336,3 +336,43 @@ TEST(segment_tree, join) {
}
}
}
+
+
+TEST(segment_tree, tag_roots) {
+ // linear chain
+ {
+ arb::segment_tree tree;
+ tree.append(arb::mnpos, {0, 0, 0}, {0, 0, 1}, 1);
+ tree.append(0, {0, 0, 1}, {0, 0, 2}, 1);
+ tree.append(1, {0, 0, 2}, {0, 0, 3}, 2);
+ tree.append(2, {0, 0, 3}, {0, 0, 4}, 3);
+ tree.append(3, {0, 0, 4}, {0, 0, 5}, 2);
+ {
+ EXPECT_EQ((std::vector<arb::msize_t>{0}), arb::tag_roots(tree, 1));
+ EXPECT_EQ((std::vector<arb::msize_t>{2, 4}), arb::tag_roots(tree, 2));
+ EXPECT_EQ((std::vector<arb::msize_t>{3}), arb::tag_roots(tree, 3));
+ }
+ }
+ // gnarly tree
+ // (npos) - 0 - 1 - 4
+ // \
+ // 2 - 3
+ // \
+ // 5
+ {
+ arb::segment_tree tree;
+ tree.append(arb::mnpos, {0, 0, 0}, {0, 0, 1}, 1); // 0
+ tree.append(0, {0, 0, 1}, {0, 0, 2}, 3); // 1
+ tree.append(0, {0, 0, 2}, {0, 0, 3}, 2); // 2
+ tree.append(2, {0, 0, 3}, {0, 0, 4}, 5); // 3
+ tree.append(1, {0, 0, 4}, {0, 0, 5}, 4); // 4
+ tree.append(2, {0, 0, 5}, {0, 0, 6}, 5); // 5
+ {
+ EXPECT_EQ((std::vector<arb::msize_t>{0}), arb::tag_roots(tree, 1));
+ EXPECT_EQ((std::vector<arb::msize_t>{2}), arb::tag_roots(tree, 2));
+ EXPECT_EQ((std::vector<arb::msize_t>{1}), arb::tag_roots(tree, 3));
+ EXPECT_EQ((std::vector<arb::msize_t>{4}), arb::tag_roots(tree, 4));
+ EXPECT_EQ((std::vector<arb::msize_t>{3, 5}), arb::tag_roots(tree, 5));
+ }
+ }
+}
--
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