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https://github.com/bencardoen/smlmvis

Superresolution visualization of 3D protein localization data from a range of microscopes

https://github.com/bencardoen/smlmvis

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: wiley.com, zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.7%) to scientific vocabulary

Keywords

microscopy smlm superresolution visualization-tools vtk
Last synced: 5 months ago · JSON representation

Repository

Superresolution visualization of 3D protein localization data from a range of microscopes

Basic Info
  • Host: GitHub
  • Owner: bencardoen
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 12.8 MB
Statistics
  • Stars: 5
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 2
Topics
microscopy smlm superresolution visualization-tools vtk
Created over 6 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

Superresolution visualization of 3D protein localization data from a range of microscopes

Anaconda-Server Badge

PyPI version

DOI

Read, analyze and visualize SMLM localization data quickly from a wide range of localization algorithms.

Paraview Example rendering of EPFL challenge dataset 'MT0.N1.HD'

See demo.ipynb for example usage.

Requirements

Python

  • vtk
  • numpy
  • jupyter
  • pandas
  • seaborn
  • requests
  • scipy

See requirements.yml for Conda, piprequirements.txt for pip

Optional

Gif of install

Supported Microscopes/Algorithms

  • EPFL Challenge:
    • epflreader.EPFLReader('data.csv')
    • source
  • Leica GSD
    • gsd.GSDReader('test.bin') # with test.desc in same folder,
    • gsdreader.GSDReader('test.ascii', preprocess=True, binary=False) # ASCII format
    • (needs pixel to nm conversion (e.g. *160 nm /px) : obj.points *= X
    • source
  • Tafteh et al dSTORM with z-drift correction (LSI - UBC)
    • dlpreader.DlpReader('test.3dlp')
    • source
  • Rainstorm
    • db = rainstormreader.RainStormReader('data.csv') # Automatically finds pixel to nm
    • source
  • Abbelight
    • ab = abbelightreader.AbbelightReader('data.csv') # in Nm
    • source

Install

PIP

bash $pip install smlmvis

Conda

bash $conda conda install -c bcardoen smlmvis

Local from git master

bash $git clone git@github.com:bencardoen/smlmvis.git $pip install .

Optional

You may want to install optional dependencies, e.g. jupyter notebook and seaborn for the demo: pip install jupyter seaborn

Usage

See demo.ipynb for example usage.

A typical workflow is * use one of the readers (e.g. GSDReader in smlmvis.gsreader) to load in the SMLM data * process the point cloud (obj.points) or compute statistics on the metadata (obj.values) * write out the data to vtk/paraview format using e.g. VtuWriter in vtuwriter

Cite

latex @misc{Cardoen2019, author = {Cardoen, Ben}, title = {Superresolution visualization of 3D protein localization data from a range of microscopes}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = 189660035 howpublished = {\url{https://github.com/bencardoen/smlmvis/}} }

Tests

See tests/test_writer.py

This will download the challenge data set, read it, decode it, write it to VTK and compare with a reference.

Acknowledgements

VTU writing code uses the VTK examples heavily to figure out how to interface with VTK. * VTK Python API

Owner

  • Name: Ben Cardoen
  • Login: bencardoen
  • Kind: user
  • Location: Vancouver
  • Company: https://github.com/sfu-mial

PhD Student Computing Science @sfu-mial Simon Fraser University

GitHub Events

Total
Last Year

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 13
  • Total Committers: 2
  • Avg Commits per committer: 6.5
  • Development Distribution Score (DDS): 0.231
Top Committers
Name Email Commits
Ben Cardoen b****n@s****a 10
bencardoen 2****n@u****m 3
Committer Domains (Top 20 + Academic)
sfu.ca: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 11 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 6
  • Total maintainers: 1
pypi.org: smlmvis

Superresolution visualization of 3D protein localization data from a range of microscopes

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 11 Last month
Rankings
Dependent packages count: 10.1%
Stargazers count: 21.5%
Dependent repos count: 21.6%
Average: 29.2%
Forks count: 29.8%
Downloads: 62.9%
Maintainers (1)
bcardoen
Last synced: 6 months ago

Dependencies

piprequirements.txt pypi
  • Jinja2 ==2.10.3
  • MarkupSafe ==1.1.1
  • Pillow ==6.2.1
  • PySocks ==1.7.1
  • Pygments ==2.4.2
  • Send2Trash ==1.5.0
  • asn1crypto ==1.2.0
  • attrs ==19.3.0
  • auto-tqdm ==1.0.3
  • backcall ==0.1.0
  • bleach ==3.1.0
  • bullet ==2.1.0
  • certifi ==2019.9.11
  • cffi ==1.13.2
  • chardet ==3.0.4
  • create-python-package ==0.3.0.0
  • cryptography ==2.8
  • cycler ==0.10.0
  • decorator ==4.4.1
  • defusedxml ==0.6.0
  • entrypoints ==0.3
  • environments-utils ==1.0.2
  • future ==0.18.2
  • idna ==2.8
  • importlib-metadata ==0.23
  • ipykernel ==5.1.3
  • ipython ==7.9.0
  • ipython-genutils ==0.2.0
  • ipywidgets ==7.5.1
  • jedi ==0.15.1
  • jsonschema ==3.1.1
  • jupyter ==1.0.0
  • jupyter-client ==5.3.4
  • jupyter-console ==6.0.0
  • jupyter-core ==4.6.1
  • kiwisolver ==1.1.0
  • matplotlib ==3.1.1
  • mistune ==0.8.4
  • mkl-fft ==1.0.15
  • mkl-random ==1.1.0
  • mkl-service ==2.3.0
  • more-itertools ==7.2.0
  • nbconvert ==5.6.1
  • nbformat ==4.4.0
  • notebook ==6.0.2
  • numpy ==1.17.2
  • olefile ==0.46
  • pandas ==0.25.3
  • pandocfilters ==1.4.2
  • parso ==0.5.1
  • patsy ==0.5.1
  • pexpect ==4.7.0
  • pickleshare ==0.7.5
  • prometheus-client ==0.7.1
  • prompt-toolkit ==2.0.10
  • ptyprocess ==0.6.0
  • pyOpenSSL ==19.0.0
  • pycparser ==2.19
  • pyparsing ==2.4.5
  • pyrsistent ==0.15.5
  • python-dateutil ==2.8.1
  • pytz ==2019.3
  • pyzmq ==18.1.0
  • qtconsole ==4.5.5
  • requests ==2.22.0
  • scipy ==1.3.1
  • seaborn ==0.9.0
  • six ==1.13.0
  • smlmvis ==0.0.8
  • statsmodels ==0.10.1
  • terminado ==0.8.2
  • testpath ==0.4.4
  • tornado ==6.0.3
  • tqdm ==4.32.2
  • traitlets ==4.3.3
  • urllib3 ==1.24.2
  • virtualenv ==16.7.2
  • vtk ==8.1.2
  • wcwidth ==0.1.7
  • webencodings ==0.5.1
  • widgetsnbextension ==3.5.1
  • zipp ==0.6.0
setup.py pypi