| Name | ArchNGV JSON |
| Version |
3.3.0
JSON |
| download |
| home_page | None |
| Summary | NGV architecture modules |
| upload_time | 2024-06-12 11:16:14 |
| maintainer | None |
| docs_url | None |
| author | Blue Brain Project, EPFL |
| requires_python | >=3.9 |
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# ArchNGV
Modules for in-silco building the Neuronal-Glial-Vascular structural architecture.
## Documentation:
* [latest](https://archngv.readthedocs.io/en/latest)
* [File Format Specification](https://sonata-extension.readthedocs.io/en/latest/index.html)
## Installation
### From PyPI
```shell
pip install archngv[all]
```
### From source
```shell
# Clone this repository
git clone https://github.com/BlueBrain/ArchNGV.git
# Create a Python virtualenv in repository source directory
python -m venv /path/to/repo/.venv
# Bring the virtualenv in this shell environment
. /path/to/repo/.venv/bin/activate
# Install ArchNGV
cd ArchNGV/
pip install .[all]
```
# Examples
## Create circuit exemplars
```shell
# Create a directory for your circuit
circuit_dir=./circuits
mkdir -p $circuit_dir
# Create an exemplar
python ./exemplar/create_exemplar.py $circuit_dir
```
## Execute cell placement
To proceed to the cell placement in one of the created exemplar:
```
# Change directory to one of the created exemplar
cd $circuit_dir/exemplar_ID
# Execute the "cell-placement" snakemake target
./run.sh cell-placement
# -> creates file build/cell_data.h5
```
Use the `cell_data_sonata` SnakeMake task to perform output conversion to Sonata format
after the cell placement:
```shell
./run.sh cell_data_sonata
# sonata file glia.h5.somata is created in the sonata.tmp directory
find build/sonata.tmp
# build/sonata.tmp
# build/sonata.tmp/nodes
# build/sonata.tmp/nodes/glia.h5.somata
```
## Astrocyte Synthesis
It uses Dask for parallel calculations.
An example for a local machine usage. Pay attention that `--parallel` option is not used:
```shell
ngv synthesis \
--config /path/to/synthesis_config.yaml' \
--tns-distributions /path/to/tns_distributions.json \
--tns-parameters /path/to/tns_parameters.json \
--tns-context /path/to/tns_context.json \
--astrocytes /path/to/glia.h5 \
--microdomains /path/to/microdomains.h5 \
--gliovascular-connectivity /path/to/gliovascular.h5 \
--neuroglial-connectivity /path/to/neuroglial.h5 \
--endfeet-areas /path/to/endfeet_areas.h5 \
--neuronal-connectivity /path/to/edges.h5 \
--out-morph-dir /path/to/out_morphologies
```
An example for a BB5 usage. `--parallel` option is used in conjunction with multiple exclusive
nodes, otherwise no benefits from `--parallel`:
```shell
srun -Aproj<your_project> -N2 -t=24:00:00 --exclusive \
ngv synthesis \
--config ... \
// all above options as before
--parallel
```
An example of Snakemake usage.
```shell
snakemake \
--snakefile snakemake/Snakefile \
--cluster-config path/to/cluster.yaml \ # see an example below
--config bioname path/to/bioname \
synthesis
```
Don't forget to have an entry in your `cluster.yaml` for `synthesis`
```yaml
synthesis:
jobname: ngv_synthesis
account: '<your project>'
nodes: <better have at least 2, 4 is recommended>
partition: 'prod'
constraint: 'cpu'
time: '04:00:00' # feel free to increase time
cpus-per-task: 2
exclusive: true
mem: 0
```
Also it is highly recommended to export in advance next Dask variables for better performance:
```shell
export DASK_DISTRIBUTED__WORKER__USE_FILE_LOCKING=False
export DASK_DISTRIBUTED__WORKER__MEMORY__TARGET=False # don't spill to disk
export DASK_DISTRIBUTED__WORKER__MEMORY__SPILL=False # don't spill to disk
export DASK_DISTRIBUTED__WORKER__MEMORY__PAUSE=0.80 # pause execution at 80% memory use
export DASK_DISTRIBUTED__WORKER__MEMORY__TERMINATE=0.95 # restart the worker at 95% use
# Reduce dask profile memory usage/leak (see https://github.com/dask/distributed/issues/4091)
export DASK_DISTRIBUTED__WORKER__PROFILE__INTERVAL=10000ms # Time between statistical profiling queries
export DASK_DISTRIBUTED__WORKER__PROFILE__CYCLE=1000000ms # Time between starting new profile
```
A final sbatch script example
```shell
#!/bin/bash
# below SBATCH options are not related to options from cluster.yaml
#SBATCH --partition prod
#SBATCH --account proj62
#SBATCH --nodes 1 # It is not recommended to set higher because synthesis task will be launched with SBATCH options from its entry in `cluster.yaml`
#SBATCH --time 08:00:00
#SBATCH --job-name sNGV
#SBATCH --output out-%J.log
#SBATCH --error err-%J.log
#SBATCH --mem 200000
#SBATCH --exclusive
export DASK_DISTRIBUTED__WORKER__USE_FILE_LOCKING=False
export DASK_DISTRIBUTED__WORKER__MEMORY__TARGET=False # don't spill to disk
export DASK_DISTRIBUTED__WORKER__MEMORY__SPILL=False # don't spill to disk
export DASK_DISTRIBUTED__WORKER__MEMORY__PAUSE=0.80 # pause execution at 80% memory use
export DASK_DISTRIBUTED__WORKER__MEMORY__TERMINATE=0.95 # restart the worker at 95% use
# Reduce dask profile memory usage/leak (see https://github.com/dask/distributed/issues/4091)
export DASK_DISTRIBUTED__WORKER__PROFILE__INTERVAL=10000ms # Time between statistical profiling queries
export DASK_DISTRIBUTED__WORKER__PROFILE__CYCLE=1000000ms # Time between starting new profile
source <venv with ArchNGV installed>/bin/activate
snakemake --snakefile <path to Snakefile of this project> \
--config bioname=<path to bioname> \
--directory <path to save results> \
--cluster-config <path to your cluster.yaml> \
-f synthesis
```
## Citation
When you use ArchNGV software or methods in your research, we ask you to cite the following publication (this includes poster presentations):
[Zisis E, Keller D, Kanari L, Arnaudon A, Gevaert M, Delemontex T, Coste B, Foni A, Abdellah M, Calì C, Hess K, Magistretti PJ, Schürmann F, Markram H. 2021. Digital Reconstruction of the Neuro-Glia-Vascular Architecture. Cerebral Cortex. 31:5686–5703.
](https://doi.org/10.1093/cercor/bhab254)
```
@article{10.1093/cercor/bhab254,
author = {Zisis, Eleftherios and Keller, Daniel and Kanari, Lida and Arnaudon, Alexis and Gevaert, Michael and Delemontex, Thomas and Coste, Benoît and Foni, Alessandro and Abdellah, Marwan and Calì, Corrado and Hess, Kathryn and Magistretti, Pierre Julius and Schürmann, Felix and Markram, Henry},
title = "{Digital Reconstruction of the Neuro-Glia-Vascular Architecture}",
journal = {Cerebral Cortex},
volume = {31},
number = {12},
pages = {5686-5703},
year = {2021},
month = {08},
issn = {1047-3211},
doi = {10.1093/cercor/bhab254},
url = {https://doi.org/10.1093/cercor/bhab254},
eprint = {https://academic.oup.com/cercor/article-pdf/31/12/5686/40814577/bhab254.pdf},
}
```
## Acknowledgements
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2017-CRG6-3438
The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.
Copyright (c) 2019-2024 Blue Brain Project/EPFL
Raw data
{
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"requires_python": ">=3.9",
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"keywords": null,
"author": "Blue Brain Project, EPFL",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/d4/be/275b3225b5d388b5d31519b47b327071912c069994d29197511676167e7f/archngv-3.3.0.tar.gz",
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"description": "# ArchNGV\nModules for in-silco building the Neuronal-Glial-Vascular structural architecture.\n\n## Documentation:\n\n* [latest](https://archngv.readthedocs.io/en/latest)\n\n* [File Format Specification](https://sonata-extension.readthedocs.io/en/latest/index.html)\n\n## Installation\n\n### From PyPI\n\n```shell\npip install archngv[all]\n```\n\n### From source\n```shell\n# Clone this repository\ngit clone https://github.com/BlueBrain/ArchNGV.git\n\n# Create a Python virtualenv in repository source directory\npython -m venv /path/to/repo/.venv\n\n# Bring the virtualenv in this shell environment\n. /path/to/repo/.venv/bin/activate\n\n# Install ArchNGV\ncd ArchNGV/\npip install .[all]\n```\n# Examples\n## Create circuit exemplars\n\n```shell\n# Create a directory for your circuit\ncircuit_dir=./circuits\nmkdir -p $circuit_dir\n\n# Create an exemplar\npython ./exemplar/create_exemplar.py $circuit_dir\n```\n\n## Execute cell placement\n\nTo proceed to the cell placement in one of the created exemplar:\n\n```\n# Change directory to one of the created exemplar\ncd $circuit_dir/exemplar_ID\n\n# Execute the \"cell-placement\" snakemake target\n./run.sh cell-placement\n# -> creates file build/cell_data.h5\n```\n\nUse the `cell_data_sonata` SnakeMake task to perform output conversion to Sonata format\nafter the cell placement:\n\n```shell\n./run.sh cell_data_sonata\n\n# sonata file glia.h5.somata is created in the sonata.tmp directory\nfind build/sonata.tmp\n# build/sonata.tmp\n# build/sonata.tmp/nodes\n# build/sonata.tmp/nodes/glia.h5.somata\n```\n\n## Astrocyte Synthesis\n\nIt uses Dask for parallel calculations.\n\nAn example for a local machine usage. Pay attention that `--parallel` option is not used:\n```shell\nngv synthesis \\\n --config /path/to/synthesis_config.yaml' \\\n --tns-distributions /path/to/tns_distributions.json \\\n --tns-parameters /path/to/tns_parameters.json \\\n --tns-context /path/to/tns_context.json \\\n --astrocytes /path/to/glia.h5 \\\n --microdomains /path/to/microdomains.h5 \\\n --gliovascular-connectivity /path/to/gliovascular.h5 \\\n --neuroglial-connectivity /path/to/neuroglial.h5 \\\n --endfeet-areas /path/to/endfeet_areas.h5 \\\n --neuronal-connectivity /path/to/edges.h5 \\\n --out-morph-dir /path/to/out_morphologies\n```\nAn example for a BB5 usage. `--parallel` option is used in conjunction with multiple exclusive\nnodes, otherwise no benefits from `--parallel`:\n```shell\nsrun -Aproj<your_project> -N2 -t=24:00:00 --exclusive \\\nngv synthesis \\\n --config ... \\\n // all above options as before\n --parallel\n```\nAn example of Snakemake usage. \n```shell\nsnakemake \\\n --snakefile snakemake/Snakefile \\\n --cluster-config path/to/cluster.yaml \\ # see an example below\n --config bioname path/to/bioname \\\n synthesis\n```\n\nDon't forget to have an entry in your `cluster.yaml` for `synthesis`\n```yaml\nsynthesis:\n jobname: ngv_synthesis\n account: '<your project>'\n nodes: <better have at least 2, 4 is recommended>\n partition: 'prod'\n constraint: 'cpu'\n time: '04:00:00' # feel free to increase time \n cpus-per-task: 2\n exclusive: true\n mem: 0\n```\n\nAlso it is highly recommended to export in advance next Dask variables for better performance:\n```shell\nexport DASK_DISTRIBUTED__WORKER__USE_FILE_LOCKING=False\nexport DASK_DISTRIBUTED__WORKER__MEMORY__TARGET=False # don't spill to disk\nexport DASK_DISTRIBUTED__WORKER__MEMORY__SPILL=False # don't spill to disk\nexport DASK_DISTRIBUTED__WORKER__MEMORY__PAUSE=0.80 # pause execution at 80% memory use\nexport DASK_DISTRIBUTED__WORKER__MEMORY__TERMINATE=0.95 # restart the worker at 95% use\n# Reduce dask profile memory usage/leak (see https://github.com/dask/distributed/issues/4091)\nexport DASK_DISTRIBUTED__WORKER__PROFILE__INTERVAL=10000ms # Time between statistical profiling queries\nexport DASK_DISTRIBUTED__WORKER__PROFILE__CYCLE=1000000ms # Time between starting new profile\n```\n\nA final sbatch script example\n```shell\n#!/bin/bash\n# below SBATCH options are not related to options from cluster.yaml \n#SBATCH --partition prod\n#SBATCH --account proj62\n#SBATCH --nodes 1 # It is not recommended to set higher because synthesis task will be launched with SBATCH options from its entry in `cluster.yaml` \n#SBATCH --time 08:00:00\n#SBATCH --job-name sNGV\n#SBATCH --output out-%J.log\n#SBATCH --error err-%J.log\n#SBATCH --mem 200000\n#SBATCH --exclusive\nexport DASK_DISTRIBUTED__WORKER__USE_FILE_LOCKING=False\nexport DASK_DISTRIBUTED__WORKER__MEMORY__TARGET=False # don't spill to disk\nexport DASK_DISTRIBUTED__WORKER__MEMORY__SPILL=False # don't spill to disk\nexport DASK_DISTRIBUTED__WORKER__MEMORY__PAUSE=0.80 # pause execution at 80% memory use\nexport DASK_DISTRIBUTED__WORKER__MEMORY__TERMINATE=0.95 # restart the worker at 95% use\n# Reduce dask profile memory usage/leak (see https://github.com/dask/distributed/issues/4091)\nexport DASK_DISTRIBUTED__WORKER__PROFILE__INTERVAL=10000ms # Time between statistical profiling queries\nexport DASK_DISTRIBUTED__WORKER__PROFILE__CYCLE=1000000ms # Time between starting new profile\n\nsource <venv with ArchNGV installed>/bin/activate\n\nsnakemake --snakefile <path to Snakefile of this project> \\\n --config bioname=<path to bioname> \\\n --directory <path to save results> \\\n --cluster-config <path to your cluster.yaml> \\\n -f synthesis\n```\n## Citation\nWhen you use ArchNGV software or methods in your research, we ask you to cite the following publication (this includes poster presentations):\n\n[Zisis E, Keller D, Kanari L, Arnaudon A, Gevaert M, Delemontex T, Coste B, Foni A, Abdellah M, Cal\u00ec C, Hess K, Magistretti PJ, Sch\u00fcrmann F, Markram H. 2021. Digital Reconstruction of the Neuro-Glia-Vascular Architecture. Cerebral Cortex. 31:5686\u20135703.\n](https://doi.org/10.1093/cercor/bhab254)\n\n```\n@article{10.1093/cercor/bhab254,\n author = {Zisis, Eleftherios and Keller, Daniel and Kanari, Lida and Arnaudon, Alexis and Gevaert, Michael and Delemontex, Thomas and Coste, Beno\u00eet and Foni, Alessandro and Abdellah, Marwan and Cal\u00ec, Corrado and Hess, Kathryn and Magistretti, Pierre Julius and Sch\u00fcrmann, Felix and Markram, Henry},\n title = \"{Digital Reconstruction of the Neuro-Glia-Vascular Architecture}\",\n journal = {Cerebral Cortex},\n volume = {31},\n number = {12},\n pages = {5686-5703},\n year = {2021},\n month = {08},\n issn = {1047-3211},\n doi = {10.1093/cercor/bhab254},\n url = {https://doi.org/10.1093/cercor/bhab254},\n eprint = {https://academic.oup.com/cercor/article-pdf/31/12/5686/40814577/bhab254.pdf},\n}\n```\n\n## Acknowledgements\n\nThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2017-CRG6-3438\n\nThe development of this software was supported by funding to the Blue Brain Project, a research center of the \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), from the Swiss government\u2019s ETH Board of the Swiss Federal Institutes of Technology.\n\nCopyright (c) 2019-2024 Blue Brain Project/EPFL\n",
"bugtrack_url": null,
"license": " Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. 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