Python data backend for neuroglancer, a WebGL-based viewer for volumetric data

Project description

Neuroglancer Python Integration

This package provides a Python interface for controlling Neuroglancer, a web-based 3-d volumetric data viewer.

The following features are supported:

Viewing in-memory NumPy arrays (or any other array type with a similar interface, including HDF5 arrays loaded through h5py)
Reading and writing the Neuroglancer viewer state from Python
Changing Neuroglancer key and mouse bindings
Defining actions (to be triggered by key or mouse bindings) that cause a Python callback to be invoked.

It starts a local web server for communicating state changes using sockjs, serving a copy of the Neuroglancer client code, and for serving data to the Neuroglancer client if Python data sources are used.

Installation

It is recommended that you activate a suitable Python virtual environment before installing.

Python 3.5 or later is required.

You can install the latest published package from PyPI with:

pip install neuroglancer

In most cases, this will use a prebuilt binary wheel, which requires neither node.js (to build the Neuroglancer client) nor a C++ compiler. If no binary wheel is available for your platform, a source distribution (sdist) will be used instead, which requires a C++ compiler to build but does not require node.js (the source distribution includes a prebuilt copy of the Neuroglancer client).

Direct installation from remote git repository

To install the latest version from the Neuroglancer git repository, you can use:

pip install git+https://github.com/google/neuroglancer

Note that installing from a git repository requires Node.js and a C++ compiler.

To install a specific commit XXXXXXXXX:

pip install git+https://github.com/google/neuroglancer@XXXXXXXXX

In another Python package, you can declare a dependency on a git version using the syntax:

setup(
    name='<package>',
    ...,
    install_requires=[
        ...,
        'neuroglancer @ git+https://github.com/google/neuroglancer@XXXXXXXXX',
    ],
)

Installation from local checkout of git repository

You can also install from a local checkout of the git repository. Two forms of installation are supported: normal installation, and an editable installation for development purposes.

As with installation from a remote git repository, installation from a local checkout requires Node.js to build the Neuroglancer client and a C++ compiler to build the C++ mesh generation extension module.

Normal installation

For normal installation, run the following from the root of the repository:

python setup.py install

That will automatically build the Neuroglancer client using Node.js if it has not already been built (i.e. if neuroglancer/static/index.html does not exist). To rebuild the Neuroglancer client explicitly, you can use:

python setup.py bundle_client

npm run build-python

Note: Installing from a local checkout using pip install . also works, but it may be slower because it makes a full copy of the local directory (https://github.com/pypa/pip/pull/7882), including the possibly-large .git and node_modules directories.

Editable installation (for development purposes)

During development, an editable installation allows the package to be imported directly from the local checkout directory:

pip install -e .

Any changes you make to the .py source files take effect the next time the package is imported, without the need to reinstall. If you make changes to the Neuroglancer client, you still need to rebuild it with npm run build-python. You can also keep the Neuroglancer client continuously up-to-date by running npm run dev-server-python.

Examples

See the example programs in the examples/ directory. Run them using the Python interpreter in interactive mode, e.g.

python -i example.py

or using the IPython magic command

%run -i python.py

Do not run an example non-interactively as

python example.py

because then the server will exit immediately.

Mesh generation

For in-memory segmentation volumes, mesh representations of the surface of each object can be generated on-demand as they are requested by the client (e.g. due to the user selecting a segment)

Security

By default the server binds only to the 127.0.0.1 address, and for protection against cross-site scripting attacks only accepts requests that include a valid randomly-generated 160-bit secret key.

Test suite

The test suite can be run using the tox command. Some of the tests require a WebGL2-enabled web browser in order to test interaction with the Neuroglancer client. Both Chrome and Firefox are supported, but currently due to bugs in Swiftshader, Chrome Headless does not work. Firefox Headless also currently does not support WebGL at all. On Linux, you can successfully run the tests headlessly on Firefox using xvfb-run. On other platforms, tests can't be run headlessly.

# For headless using Firefox on xvfb (Linux only)
sudo apt-get instrall xvfb # On Debian-based systems
tox -e firefox-xvfb  # Run tests using non-headless Firefox

# For non-headless using Chrome
tox -e chrome

# For non-headless using Firefox
tox -e firefox

# To run only tests that do not require a browser
tox -e skip-browser-tests

Refer to tox.ini for details of the test procedure.

Project details

Release history Release notifications | RSS feed

2.40.1

Jul 23, 2024

2.39.2

Mar 18, 2024

2.39.1

Feb 24, 2024

2.39

Jan 8, 2024

2.38

Dec 26, 2023

2.37

Oct 7, 2023

2.36

Mar 15, 2023

2.35

Feb 8, 2023

2.34

Jan 19, 2023

This version

2.33

Jan 19, 2023

2.32

Nov 29, 2022

2.31

Oct 26, 2022

2.30

Aug 31, 2022

2.29

Jul 19, 2022

2.28

Jun 17, 2022

2.27

May 20, 2022

2.26.post1.dev0 pre-release

May 11, 2022

2.25.post1.dev0 pre-release

Apr 5, 2022

2.24.post1.dev0 pre-release

Feb 17, 2022

2.23.post1.dev0 pre-release

Oct 16, 2021

2.22

May 27, 2021

2.21

May 5, 2021

2.20

Jan 18, 2021

2.19

Dec 4, 2020

2.18

Dec 3, 2020

2.17

Nov 16, 2020

2.16

Nov 13, 2020

2.15

Nov 1, 2020

2.14

Oct 26, 2020

2.13

Oct 8, 2020

2.12

Oct 5, 2020

2.11

Sep 14, 2020

2.10

Aug 11, 2020

2.9

Aug 5, 2020

2.8

Jul 17, 2020

2.7

Jun 30, 2020

2.6

May 19, 2020

2.5

May 13, 2020

2.4

May 11, 2020

2.3

Feb 5, 2020

2.2

Jan 21, 2020

2.1

Jan 16, 2020

2.0

Jan 7, 2020

1.1.6

Oct 25, 2019

1.1.4

Aug 21, 2019

1.1.3

Aug 12, 2019

1.1.2

May 30, 2019

1.1.1

May 23, 2019

1.1.0

May 18, 2019

1.0.15

Apr 11, 2019

1.0.14

Apr 8, 2019

1.0.13

Feb 1, 2019

1.0.12

Feb 1, 2019

1.0.11

Nov 6, 2018

1.0.10

Jun 20, 2018

1.0.9

May 26, 2018

1.0.8

Apr 26, 2018

1.0.7

Mar 8, 2018

1.0.6

Jan 26, 2018

1.0.5

Jan 26, 2018

1.0.4

Jan 26, 2018

1.0.3

Jan 26, 2018

1.0.2

Nov 29, 2017

1.0.1

Nov 28, 2017

0.0.8

Sep 27, 2016

0.0.7

Jun 30, 2016

0.0.6

Jun 27, 2016

0.0.5

Jun 20, 2016

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neuroglancer-2.33.tar.gz (2.6 MB view details)

Uploaded Jan 19, 2023 Source

Built Distributions

neuroglancer-2.33-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.11 Windows x86-64

neuroglancer-2.33-cp311-cp311-musllinux_1_1_x86_64.whl (4.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.11 musllinux: musl 1.1+ x86-64

neuroglancer-2.33-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB view details)

Uploaded Jan 19, 2023 CPython 3.11 manylinux: glibc 2.17+ x86-64

neuroglancer-2.33-cp311-cp311-macosx_11_0_arm64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.11 macOS 11.0+ ARM64

neuroglancer-2.33-cp311-cp311-macosx_10_9_x86_64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.11 macOS 10.9+ x86-64

neuroglancer-2.33-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.10 Windows x86-64

neuroglancer-2.33-cp310-cp310-musllinux_1_1_x86_64.whl (4.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.10 musllinux: musl 1.1+ x86-64

neuroglancer-2.33-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB view details)

Uploaded Jan 19, 2023 CPython 3.10 manylinux: glibc 2.17+ x86-64

neuroglancer-2.33-cp310-cp310-macosx_11_0_arm64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.10 macOS 11.0+ ARM64

neuroglancer-2.33-cp310-cp310-macosx_10_9_x86_64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.10 macOS 10.9+ x86-64

neuroglancer-2.33-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.9 Windows x86-64

neuroglancer-2.33-cp39-cp39-musllinux_1_1_x86_64.whl (4.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.9 musllinux: musl 1.1+ x86-64

neuroglancer-2.33-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB view details)

Uploaded Jan 19, 2023 CPython 3.9 manylinux: glibc 2.17+ x86-64

neuroglancer-2.33-cp39-cp39-macosx_11_0_arm64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.9 macOS 11.0+ ARM64

neuroglancer-2.33-cp39-cp39-macosx_10_9_x86_64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.9 macOS 10.9+ x86-64

neuroglancer-2.33-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.8 Windows x86-64

neuroglancer-2.33-cp38-cp38-musllinux_1_1_x86_64.whl (4.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.8 musllinux: musl 1.1+ x86-64

neuroglancer-2.33-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB view details)

Uploaded Jan 19, 2023 CPython 3.8 manylinux: glibc 2.17+ x86-64

neuroglancer-2.33-cp38-cp38-macosx_11_0_arm64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.8 macOS 11.0+ ARM64

neuroglancer-2.33-cp38-cp38-macosx_10_9_x86_64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.8 macOS 10.9+ x86-64

neuroglancer-2.33-cp37-cp37m-win_amd64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.7m Windows x86-64

neuroglancer-2.33-cp37-cp37m-musllinux_1_1_x86_64.whl (4.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.7m musllinux: musl 1.1+ x86-64

neuroglancer-2.33-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB view details)

Uploaded Jan 19, 2023 CPython 3.7m manylinux: glibc 2.17+ x86-64

neuroglancer-2.33-cp37-cp37m-macosx_10_9_x86_64.whl (2.6 MB view details)

Uploaded Jan 19, 2023 CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file neuroglancer-2.33.tar.gz.

File metadata

Download URL: neuroglancer-2.33.tar.gz
Upload date: Jan 19, 2023
Size: 2.6 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for neuroglancer-2.33.tar.gz
Algorithm	Hash digest
SHA256	`993a5a5cb47de82101400563557f25a3d3d14ad849d5e5aa1c6e90993cb93366`
MD5	`6f4e7ca84cc882283dc990577e458dd7`
BLAKE2b-256	`44b5f889a0888f9bca86eb83cf2ee18f148cbe04af2554ad6671237fd5c9f9cf`

Algorithm	Hash digest
SHA256	`b500b1b7f053db836b9f3884a98c164c3b177e130eb46a92069aff1d0ffd7b13`
MD5	`7acc1309d75241bf197971800a336f44`
BLAKE2b-256	`3c1bcb5937c42cb5e347274321c62843f009dbc570a91b6d56fe9a07f76de01f`

Algorithm	Hash digest
SHA256	`607d838702d64f0f44ff094a51179d6d63aa95bdbee76929c0ec9b3ddb48c098`
MD5	`c4117e64acefcde148e82fd7cc3e8753`
BLAKE2b-256	`43e952c1ce43b1521a43df3d18c7fa8227cd1beeca12ce2c549c218369de9eb8`

Algorithm	Hash digest
SHA256	`f3eede292ad0551cdadc8b7bfb60e6f01f09517ba6820a385d8030f42c66d080`
MD5	`93c273dc3b29e10a72fa930ea5be7e4c`
BLAKE2b-256	`8732b5ceebbeb481eae8dea7fa6bac90e3f0eb4dc391571f03be13a265f276c1`

Algorithm	Hash digest
SHA256	`099894f020ff68f00ce526d9914eaadae0297e7c609dbdc23ad1e599e713dde9`
MD5	`7c3cca7db19b31790083cf018a99b558`
BLAKE2b-256	`0b22765c742dfc76bbea9400ab2161266427113c0ae900ba3293f93b3ef2db5a`

Algorithm	Hash digest
SHA256	`7099e6be3208d671548fa5a9654505a5f12884d9498bdcd0bfd5015c63dafa8c`
MD5	`92accb5a66a0ee2f0d44e2b24f75799d`
BLAKE2b-256	`802e0a195238f949f276e1f7bdf3a58d7e6322b92d7fa3c71477f8418e31634b`

Algorithm	Hash digest
SHA256	`2d7655bb028c387d89471fbe26c1578e22e8d3877f97299b646bdb91febb0c5b`
MD5	`215655ae9b43290803400ad5bfa1be09`
BLAKE2b-256	`3c3878547f28c152dc4fac7c8cea7e76b00826d819e3d9b6fa82945aee869a96`

Algorithm	Hash digest
SHA256	`de40b61e9502b8af600e3c69dedaefd33d88db3b89dc552d3d4bc10154610d90`
MD5	`e422e8f873b431e93ae648dc64d3b601`
BLAKE2b-256	`1a17e97dbdf6c1b1909b44b924958e4322024bf4cacc495f6ca344a6db508d24`

Algorithm	Hash digest
SHA256	`077c5d86d93f7588ed50b84a165b3f6ac784b78dfbca78cab41bf05c9bf1ea1e`
MD5	`72cf59185765806d73599923c6782dd9`
BLAKE2b-256	`0ab5e86b2f661c562372747340826ccdf3fc72ac9a30fb7b9180bf1f175b1927`

Algorithm	Hash digest
SHA256	`9f706e6a49810800cf834833c4ead24519d48e868703fb86b1ad856099e058ae`
MD5	`1dce0ed2d1747ab6e7851272cf53ba3d`
BLAKE2b-256	`06de541833e6f9d86fb1c6b447a93816c731ab8af6159d70ef046c6b52501bf6`

Algorithm	Hash digest
SHA256	`a55c980f7848a325da11d079ac220f2925eaaaffb846e0d2bb9bc6bd6f76067e`
MD5	`6f66a8b3c5b0a6cf11492401688b9649`
BLAKE2b-256	`3aeca591907e5426d67f4d0c7b1fe21b2965afebecaf846fbda69e6906111ea1`

Algorithm	Hash digest
SHA256	`b41a6ac0171ae9bd6ec06cca05a43133457754697c3a11c6a8511ea9a33e6db3`
MD5	`d32a152bc09a348cb180e8f886f3db23`
BLAKE2b-256	`4f0ca1cd8342da71bf68aca24a78613d025ec622709c40e549b76fb3f4b3a500`

Algorithm	Hash digest
SHA256	`9dd1ecfb3229d486731e4bed18f2c14bc12799cdd0d2e4c4b55346be1decd60c`
MD5	`60cf7686a23dc49b4db2c676c4a56f23`
BLAKE2b-256	`e34e5c96b076a59a50ccdd93b14b0efa056c7d1140b6d82f699348802459b733`

Algorithm	Hash digest
SHA256	`b1744d4693bdb0afa35be49a6bff00398ed62a636e6c13979bac7b6ec01e6a99`
MD5	`60689d73c6035134960805af09bb1d89`
BLAKE2b-256	`abd0f54a37a2ffcaafc5c359f19989bbdb4810e34cc663cd847de5d81c544718`

Algorithm	Hash digest
SHA256	`783382475d5fdefff641c179cb46df6607844b59e79049f5cd62b6cf6e9574cb`
MD5	`2705d814fdf81d1bf5e8a230b728ba2a`
BLAKE2b-256	`0157b5deb6ae829e8164a85b5aeb11ab3440f2ca670b462ecde1ed9d65f5102b`

Algorithm	Hash digest
SHA256	`2f1bbc9a5b629acd137a2cd1e7700960779185f406398eadb38e06cb4fc4aeb0`
MD5	`a1f7a041f70a7d4b57fddeb6af0e2e3c`
BLAKE2b-256	`fa9355a66b1aeedf7b12616f209273a5518511e140653a8cdee276321d9b0326`

Algorithm	Hash digest
SHA256	`5a52b3739b50b0bf6d6a65a9ea9c66dbb1ba8f915e844417045fb17420cf5c78`
MD5	`177dc3c341a08b9239e733e5c4a6000f`
BLAKE2b-256	`4940b88c2598548e400bc8f38c99afdf968f69d83b137fbc4a4b8969264856dc`

Algorithm	Hash digest
SHA256	`68022b6e8b617429c36da34c30279b2f2a13d4bf231e6f886ccdb827c3a234eb`
MD5	`552a903c5c60a172c28c31758a84a8a6`
BLAKE2b-256	`c920306a9c9f01f2700fb704359596c8a556829136917c80822bd6ee5bee17ae`

Algorithm	Hash digest
SHA256	`50455be18c93aeb0b2efb462a29b0e92ed7200a33d5661d65445d7e4d8358127`
MD5	`c08e86e8b6d870c294e6ad5bc0667329`
BLAKE2b-256	`8e23692a5bf381d1b7712c2653ef5f4a4657563677844dfd8c6f4649d9a7e9b9`

Algorithm	Hash digest
SHA256	`9c0c4caa45a8d2d345f6178c59856a6013db8a85b65ad88f413250a548d5f91d`
MD5	`89b15dacc28f32c7b0a4f3374aa57832`
BLAKE2b-256	`29bc26c66eb51f4772b41a621c8c5ef06d78355c57c75732946203ac813c2911`

Algorithm	Hash digest
SHA256	`c65eff5b730f9fa094e12b48c1f36ab2338ad1ffe1745a0bd9f9b7f9d0f1b97b`
MD5	`51f5223d52f34e1d3a72d3b702d7d591`
BLAKE2b-256	`4755e8145ffd95f27cf8eda3d0d43095d88ec6a9cdb068f68b86eff9a4995623`

neuroglancer 2.33

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Project description

Neuroglancer Python Integration

Installation

Direct installation from remote git repository

Installation from local checkout of git repository

Normal installation

Editable installation (for development purposes)

Examples

Mesh generation

Security

Test suite

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

Algorithm	Hash digest
SHA256	`b232879772c43211abfb1963eacd7f18313df1ba3dda8abacff7d337d9f5ebb7`
MD5	`c88e3d56449d31d17617858ed30e3b1d`
BLAKE2b-256	`eda91c9aa5ef85a8a2bde3cd007daa32a1c0830a1f3f0fafe2d9a0298dce08cc`

Algorithm	Hash digest
SHA256	`4b91ddee4e869146d543d57e4cd3146b4d29175058effc4d55c730831647a2e5`
MD5	`a4c3bb9099006d5b10d80c84d2edcbf1`
BLAKE2b-256	`91686160082b8f97b2fe6a1f46348cd6c1c31b5d6a1e8bebb7e00a3d7f4e48e4`

Algorithm	Hash digest
SHA256	`f7b109e011707fc9e231cbc20f481be8d3a186b253ef60e06f840ce207724e81`
MD5	`2d7d8c2c89c1ee453975c911e104e9cd`
BLAKE2b-256	`18e0f914f5170d33249534cb60f1a82f95e5354775a984a7f869c3adcc736291`

Algorithm	Hash digest
SHA256	`86026611e0b904124ea7058b2e06e3e47616765f7b689dad09beca2cde6a78e0`
MD5	`f1763b7c9072c204b1b77e4eac5a7a44`
BLAKE2b-256	`57672ccb26a74eed35e7dab9a31fb8d0fec8d4e21f5bbd547303b56c378edd4d`