Skip to main content

Library for real-time deformability cytometry (RT-DC)

Project description

PyPI Version Build Status Coverage Status Docs Status

This is a Python library for the post-measurement analysis of real-time deformability cytometry (RT-DC) datasets; an essential part of Shape-Out.

Documentation

The documentation, including the code reference and examples, is available at dclab.readthedocs.io.

Installation

pip install dclab[all]

For more options, please check out the documentation.

Information for developers

Contributing

The main branch for developing dclab is master. If you want to make small changes like one-liners, documentation, or default values in the configuration, you may work on the master branch. If you want to change more, please (fork dclab and) create a separate branch, e.g. my_new_feature_dev, and create a pull-request once you are done making your changes. Please make sure to edit the Changelog.

Very important: Please always try to use

git pull --rebase

instead of:

git pull

to prevent non-linearities in the commit history.

Tests

dclab is tested using pytest. If you have the time, please write test methods for your code and put them in the tests directory. To run the tests, install pytest and run:

pytest tests

Docs

The docs are built with sphinx. Please make sure they compile when you change them (this also includes function doc strings):

cd docs
pip install -r requirements.txt
sphinx-build . _build  # open "index.html" in the "_build" directory

PEP8

We use flake8 to enforce coding style:

pip install flake8
flake8 --exclude _version.py dclab
flake8 docs
flake8 examples
flake8 tests

Incrementing version

Dclab gets its version from the latest git tag. If you think that a new version should be published, create a tag on the master branch (if you have the necessary permissions to do so):

git tag -a "0.1.3"
git push --tags origin

Appveyor and GitHub Actions will then automatically build source package and wheels and publish them on PyPI.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

dclab-0.58.4.tar.gz (4.9 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

dclab-0.58.4-cp312-cp312-musllinux_1_1_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.12musllinux: musl 1.1+ x86-64

dclab-0.58.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

dclab-0.58.4-cp312-cp312-macosx_10_9_x86_64.whl (928.0 kB view details)

Uploaded CPython 3.12macOS 10.9+ x86-64

dclab-0.58.4-cp311-cp311-win_amd64.whl (891.5 kB view details)

Uploaded CPython 3.11Windows x86-64

dclab-0.58.4-cp311-cp311-musllinux_1_1_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.11musllinux: musl 1.1+ x86-64

dclab-0.58.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

dclab-0.58.4-cp311-cp311-macosx_10_9_x86_64.whl (926.8 kB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

dclab-0.58.4-cp310-cp310-win_amd64.whl (891.4 kB view details)

Uploaded CPython 3.10Windows x86-64

dclab-0.58.4-cp310-cp310-musllinux_1_1_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.10musllinux: musl 1.1+ x86-64

dclab-0.58.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

dclab-0.58.4-cp310-cp310-macosx_10_9_x86_64.whl (927.6 kB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

dclab-0.58.4-cp39-cp39-win_amd64.whl (893.1 kB view details)

Uploaded CPython 3.9Windows x86-64

dclab-0.58.4-cp39-cp39-musllinux_1_1_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.9musllinux: musl 1.1+ x86-64

dclab-0.58.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

dclab-0.58.4-cp39-cp39-macosx_10_9_x86_64.whl (929.3 kB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

dclab-0.58.4-cp38-cp38-win_amd64.whl (893.4 kB view details)

Uploaded CPython 3.8Windows x86-64

dclab-0.58.4-cp38-cp38-musllinux_1_1_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.8musllinux: musl 1.1+ x86-64

dclab-0.58.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

dclab-0.58.4-cp38-cp38-macosx_10_9_x86_64.whl (926.6 kB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file dclab-0.58.4.tar.gz.

File metadata

  • Download URL: dclab-0.58.4.tar.gz
  • Upload date:
  • Size: 4.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.10.12

File hashes

Hashes for dclab-0.58.4.tar.gz
Algorithm Hash digest
SHA256 91796e822ef75a28b6b43f37b635931c52aaa3319953014a79a2fd66df088137
MD5 31779043a38ea6e7925fb5662b4a1847
BLAKE2b-256 4bd402447c6ba8431ad9a0f302c183eea8309391ab13580432bbb33ebc01031c

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp312-cp312-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp312-cp312-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 efda4530be4a127c2f34651e58695006ca9b0fad5f425ad8c84b0b61c788c8b1
MD5 93f56cca273b71301ec40b5b7b6381be
BLAKE2b-256 ce8ea724b0f256cd5f1255faefa89ada4d20f016c1bd640c882885abe01d53c3

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 68a2eacaa500043d07f96d5776d5885aafec4b518775c25f2ebb0edbc1a9c082
MD5 35d9987abc3dfea1017074a5055c3b9a
BLAKE2b-256 6e7aafffd27e60432a82cb24a7da98526362aa61346ff6fb7e9e66335393e36e

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp312-cp312-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 74ef3416e22dc16d89413a8420bcc54a899c98e6e6826f8423eb5971c05083c0
MD5 bce50692fba4b08b9463d118778da62f
BLAKE2b-256 a9859644010c82f170705547ab76334ada93d55a065692e1dd13f8ee4d31f7be

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: dclab-0.58.4-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 891.5 kB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.9

File hashes

Hashes for dclab-0.58.4-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 6121a50a5f8ed1ec6ebb1529a1c33a82247a883c4df540196e6a41963114ef4e
MD5 11c76070e95d9a1a72b22098856e2611
BLAKE2b-256 88143cd415fa73d44a0f6710281947ec57e354f1e626bbdc9ab60deb7501cc61

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp311-cp311-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp311-cp311-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 77ea03f5647970a67e04dfef1b303af0999b63e0895840a67cdd6920395b0ef5
MD5 c7cbe735d6655715d4a538c52dc3e220
BLAKE2b-256 89fab52139f7f27f7394908ce80151c762adcf8a8c1468d3e38268ff42a99c90

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 88c989cf6e2290938f4cee35fad2c1307e42108dd327997ee1fc41325aeaa9d6
MD5 c07cfb606d3cbfcf9a57d72bfa1eef74
BLAKE2b-256 f4e6747f9a70ab9ed8a27fa25e5cda5ffeda77fa50bc7c6113c0914106c658cf

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4c40a11893b9bcc31071f3fd33ea124f665c1394f086ed4a27c218e04ef596d0
MD5 444d08fb6f869804c221220112c53851
BLAKE2b-256 45d7de84f75c4154b63534c76830c50d1629808db3921f7ff79d11b0bc8cb095

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: dclab-0.58.4-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 891.4 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.9

File hashes

Hashes for dclab-0.58.4-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 5f95b53f571a3a99626aae69d43cfafd9ab298fc0f8e4dcfe6149434baaf1a55
MD5 f2bda2bb56931d120ed9438189aa77f7
BLAKE2b-256 1501555ba47368e2df8da183f97b6a3aeeef8a9d335f85fb5eca218291f66492

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 f18ab91a3b5591f89a9ba9edb32be3f8817cb526b504b35b2b7301736af9b812
MD5 67b8029c54806d7a71b58dbfccfd3524
BLAKE2b-256 e5ba34f01cdac13f02de049b957d373d91e0c4095625556df8efd75ce0e6e7c7

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 767127ce041dcca94d62dab7c3178d6bc54616c72f0559512393001ed31e77e3
MD5 c4db685f8d741e517a57398656096ffe
BLAKE2b-256 2accfbcb0b28ea442af2e385156e23c1262e56e756fda242821dc65bbcca5cf4

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9f05f26adaec8a8a308e8cd2479dbdf842792c5af6a4a1aecbabacbd865ecc0b
MD5 82a26ebf8db73d4a7b5fb88f70bf0e0d
BLAKE2b-256 90220e31c236815efe69c03bb130d97d7f4b7e4a4f2c5a99b39ecb197665cbd2

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: dclab-0.58.4-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 893.1 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.9

File hashes

Hashes for dclab-0.58.4-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 6b9a7f8ffb670e7d981457367a46c07193a797e51900e99ff30d7590f8e629a8
MD5 ec2a9411ff426567525f30146cf835bc
BLAKE2b-256 c6c3bb4c02770922686f5d63a33e3e9c0a6faa304521a38fd885cef7e339f1fe

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 bf04f99e48cfd7143c040197f1bf33dd938fa8f0c47177c198ec5957206ab9ef
MD5 d746e620d215f328ca71d4f226686e96
BLAKE2b-256 30d39f964fe876e5f70e364a5386db323d2fb6e6d571734efa525c321691c26f

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d831aedc2536038b9df3c271bf16e094ca3c62c00191a7cc90d877cd7590f842
MD5 d5a7571bd861a8d896cc31aa1adcfcee
BLAKE2b-256 ddab6922d97e5d4bfcb7204ed92460f64f5fed6da9567ba8224c0f404a0e1548

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 aef6b1d49bbdc9e50985f99e0cbe60900ace3144438bbb42a5300dbbb1c87251
MD5 1a8403b5fa7518d7ee144ea51ed9233b
BLAKE2b-256 a699a3033e540d53bd2e67d6947aca054641cb86e57f305d6a9de4a97ecf7a86

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: dclab-0.58.4-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 893.4 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.9

File hashes

Hashes for dclab-0.58.4-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 92e31371a0f4f1a0c5f596fe0cf970583119b726787092810ab5931486bb90bf
MD5 82dfccff4c82616e196418973f6e306c
BLAKE2b-256 f1fa3b4d64cb808adf04628e5e79c67e3b02deb96f00701ff465c5629453af83

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp38-cp38-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 d480cb82beaf14cb4bbf9bd800185322fb690323fc7b09a11536a6e2ffca19eb
MD5 e796dd15e10005dd5e929c9e37311ece
BLAKE2b-256 b6dd5ea80746cd9cf54691f772e440c3f0f49a7dc164806b766789aec669ad27

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 89e051f4e6141732e430bd40d831a90fb4cf4458a879d5f374fd674f2121b48d
MD5 29fdd91549a958db2560c4211bc772c9
BLAKE2b-256 7599376d244496c1c0c4251d56f4ee4942b6a42f0471b817ec3071b81304ebef

See more details on using hashes here.

File details

Details for the file dclab-0.58.4-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for dclab-0.58.4-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 57712628288cbd58e96d193e5d134f202b4e95392e15390ba48dc9d6d196e1cc
MD5 0241df11d95f31cd49b6f2c35abf57b1
BLAKE2b-256 8428f71a681285c0c478fe514e0bb482a1c27ce39152d3c912713c2470196a22

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page