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quanfima

Quanfima (Quantitative Analysis of Fibrous Materials)

https://github.com/rshkarin/quanfima

Science Score: 23.0%

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    CITATION.cff file
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    DOI references
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    Links to: zenodo.org
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    Scientific vocabulary similarity
    Low similarity (13.9%) to scientific vocabulary

Keywords

data-analysis material-science morphological-analysis volumetric-data
Last synced: 6 months ago · JSON representation

Repository

Quanfima (Quantitative Analysis of Fibrous Materials)

Basic Info
Statistics
  • Stars: 29
  • Watchers: 2
  • Forks: 17
  • Open Issues: 6
  • Releases: 1
Topics
data-analysis material-science morphological-analysis volumetric-data
Created almost 8 years ago · Last pushed about 2 years ago
Metadata Files
Readme License

README.rst 

.. image:: docs/source/_static/logo.png
    :align: left
    
-----------

.. image:: https://travis-ci.org/rshkarin/quanfima.svg?branch=master 
    :target: https://travis-ci.org/rshkarin/quanfima 
    
.. image:: https://readthedocs.org/projects/quanfima/badge/?version=latest 
    :target: http://quanfima.readthedocs.io/en/latest/?badge=latest 
    :alt: Documentation Status
    
.. image:: https://zenodo.org/badge/127795855.svg
   :target: https://zenodo.org/badge/latestdoi/127795855




*Quanfima* (**qu**\ antitative **an**\ alysis of **fi**\ brous **ma**\ terials)
is a collection of useful functions for morphological analysis and visualization
of 2D/3D data from various areas of material science. The aim is to simplify
the analysis process by providing functionality for frequently required tasks
in the same place.

More examples of usage you can find in the documentation.

- Analysis of fibrous structures by tensor-based method in 2D / 3D datasets.
- Estimation of structure diameters in 2D / 3D by a ray-casting method.
- Counting of particles in 2D / 3D datasets and providing a detailed report in
  pandas.DataFrame format.
- Calculation of porosity measure for each material in 2D / 3D datasets.
- Visualization in 2D / 3D using matplotlib, visvis packages.

Installation
------------

The easiest way to install the latest version is by using pip::

    $ pip install quanfima

You may also use Git to clone the repository and install it manually::

    $ git clone https://github.com/rshkarin/quanfima.git
    $ cd quanfima
    $ python setup.py install

Usage
-----
Open a grayscale image, perform segmentation, estimate porosity, analyze fiber
orientation and diameters, and plot the results.

.. code-block:: python

  import numpy as np
  from skimage import io, filters
  from quanfima import morphology as mrph
  from quanfima import visualization as vis
  from quanfima import utils

  img = io.imread('../data/polymer_slice.tif')

  th_val = filters.threshold_otsu(img)
  img_seg = (img > th_val).astype(np.uint8)

  # estimate porosity
  pr = mrph.calc_porosity(img_seg)
  for k,v in pr.items():
    print 'Porosity ({}): {}'.format(k, v)

  # prepare data and analyze fibers
  data, skeleton, skeleton_thick = utils.prepare_data(img_seg)
  cskel, fskel, omap, dmap, ovals, dvals = \
                      mrph.estimate_fiber_properties(data, skeleton)

  # plot results
  vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°',
                           figsize=(10,10),
                           name='2d_polymer',
                           output_dir='/path/to/output/dir')
  vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow',
                        name='2d_polymer',
                        output_dir='/path/to/output/dir')
                        
.. code-block:: python

  >> Porosity (Material 1): 0.845488888889

.. image:: docs/source/_static/2d_polymer_data.png
    :align: center
    
.. image:: docs/source/_static/2d_polymer_orientation_map_600px.png
    :align: center
    
.. image:: docs/source/_static/2d_polymer_diameter_map_600px.png
    :align: center

Owner

  • Name: Roman Shkarin
  • Login: rshkarin
  • Kind: user
  • Location: Berlin
  • Company: ML Engineer @ Remerge

Distributed systems, Machine Learning, Go, Python, Neovim

GitHub Events

Total
  • Fork event: 1
Last Year
  • Fork event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 27
  • Total Committers: 4
  • Avg Commits per committer: 6.75
  • Development Distribution Score (DDS): 0.222
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Roman Shkarin m****9@g****m 21
Vanshika Gupta v****1@g****m 3
Roman Shkarin r****n@g****m 2
Nicholas Won d****7@g****m 1

Issues and Pull Requests

Last synced: over 1 year ago

All Time
  • Total issues: 6
  • Total pull requests: 4
  • Average time to close issues: 2 months
  • Average time to close pull requests: 3 months
  • Total issue authors: 6
  • Total pull request authors: 4
  • Average comments per issue: 2.17
  • Average comments per pull request: 0.25
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • javad-rahmannezhad (1)
  • SantiagoQuinteros (1)
  • vansjyo (1)
  • CMollier (1)
  • Akhsoul (1)
  • robintwhite (1)
Pull Request Authors
  • vansjyo (1)
  • dependabot[bot] (1)
  • NicholasWon47 (1)
  • rshkarin (1)
Top Labels
Issue Labels
Pull Request Labels
dependencies (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 11 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 3
  • Total maintainers: 1
pypi.org: quanfima

The package for morphological analysis and visualization of fibrous materials.

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 11 Last month
Rankings
Forks count: 8.9%
Dependent packages count: 10.0%
Stargazers count: 12.4%
Average: 17.5%
Dependent repos count: 21.7%
Downloads: 34.6%
Maintainers (1)
rshkarin
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • matplotlib ==2.0.2
  • nose ==1.3.7
  • numpy ==1.13.3
  • pandas ==0.19.2
  • scikit-image ==0.12.3
  • scikit-learn ==0.18.1
  • scipy ==0.19.0
setup.py pypi
  • imageio >=2.3.0
  • matplotlib ==2.0.2
  • numpy >=1.13.3
  • pandas >=0.19.2
  • scikit-image >=0.12.3
  • scikit-learn >=0.18.1
  • scipy >=0.19.0