Metadata-Version: 2.1
Name: Qiber3D
Version: 0.5.2
Summary: Automated quantification of fibrous networks
Home-page: https://github.com/theia-dev/Qiber3D
Author: Anna Jaeschke; Hagen Eckert
Author-email: 
License: MIT
Download-URL: https://github.com/theia-dev/Qiber3D/archive/v0.5.2.zip
Description: # Qiber3D <img src='https://github.com/theia-dev/Qiber3D/raw/master/docs/img/synthetic_animation_silver.gif' align="right"/>
        Automated quantification of fibrous networks
        
        [![PyPi](https://img.shields.io/pypi/v/qiber3d.svg?style=for-the-badge)](https://pypi.org/project/Qiber3D/)
        [![Status](https://img.shields.io/pypi/status/qiber3d.svg?style=for-the-badge)](https://pypi.org/project/Qiber3D/)
        
        [![Documentation](https://img.shields.io/readthedocs/qiber3d.svg?style=for-the-badge)](https://Qiber3D.readthedocs.io)
        
        [![License](https://img.shields.io/github/license/theia-dev/qiber3d.svg?style=for-the-badge)](https://github.com/theia-dev/Qiber3D/blob/master/LICENSE.txt)
        [![Github issues](https://img.shields.io/github/issues/theia-dev/qiber3d.svg?style=for-the-badge)](https://github.com/theia-dev/Qiber3D/issues)
        
        [![Coverage](https://img.shields.io/codecov/c/gh/theia-dev/Qiber3D?token=UCNHVP172J&style=for-the-badge)](https://app.codecov.io/gh/theia-dev/Qiber3D)
        [![Build](https://img.shields.io/github/workflow/status/theia-dev/Qiber3D/Qiber3D.svg?style=for-the-badge)](https://github.com/theia-dev/Qiber3D/actions/workflows/test-Qiber3D.yml)
        
        
        ## Setup
            pip install Qiber3D
            
        You can also install the latest version directly from GitHub.
        
            pip install -U git+https://github.com/theia-dev/Qiber3D.git#egg=Qiber3D
        
            
        ## Usage
        
        An image stack, or a preprocessed network can be loaded with ``Network.load()``
        To follow this example you can download the image stack from figshare under [doi:10.6084/m9.figshare.13655606](https://doi.org/10.6084/m9.figshare.13655606).
        
        ```python
        import logging
        from Qiber3D import Network, config
        
        config.extract.nd2_channel_name = 'FITC'
        config.log_level = logging.DEBUG
        
        net = Network.load('microvascular_network.nd2')
        print(net)
        # Input file: microvascular_network.nd2
        #   Number of fibers: 459 (clustered 97)
        #   Number of segments: 660
        #   Number of branch points: 130
        #   Total length: 16056.46
        #   Total volume: 1240236.70
        #   Average radius: 4.990
        #   Cylinder radius: 4.959
        #   Bounding box volume: 681182790
        
        net.save(save_steps=True)
        # Qiber3D_core [INFO] Network saved to Exp190309_PrMECs-NPF180_gel4_ROI-c.qiber
        
        net.render.show()
        net.render.compare()
        ```
        
        A quick way to explore the possibilities of ``Qiber3D`` is the use of the synthetic network.
        ```python
        from Qiber3D import IO
        net = IO.load.synthetic_network()
        print(net)
        # Input file: memory
        #   Number of fibers: 4 (clustered 2)
        #   Number of segments: 11
        #   Number of branch points: 5
        #   Total length: 1141.44
        #   Total volume: 4688.67
        #   Average radius: 0.936
        #   Cylinder radius: 1.143
        #   Bounding box volume: 806162
        net.length
        # 1141.437678088988
        net.volume
        # 4688.667104530579
        net.fiber[0]
        # Fiber 0 l=451.65, V=1651.71
        print(net.fiber[0])
        # Fiber ID: 0
        #   Number of segments: 5
        #   Total length: 451.65
        #   Total volume: 1651.71
        #   Average radius: 0.86
        #   Cylinder radius: 1.08
        net.fiber[0].segment
        # {0: Segment 0 l=40.19, V=17.30, 1: Segment 1 l=124.71, V=113.51, 2: Segment 2 l=32.72, V=112.79, 
        #  3: Segment 3 l=154.15, V=1080.55, 4: Segment 4 l=99.88, V=327.56}
        print(net.segment[1])
        # Segment ID: 1
        #   Number of parts: 200
        #   Total length: 124.71
        #   Total volume: 113.51
        #   Average radius: 0.51
        #   Cylinder radius: 0.54
        net.render.show()
        net.render.show(color_mode='segment', color_map='hsv')
        net.figure.directions(out_path=None)
        ```
        
        The full source code can be accessed on [GitHub](https://github.com/theia-dev/Qiber3D) with the corresponding documentation hosted at [Read the docs](https://Qiber3D.readthedocs.io).
        
Keywords: Skeleton,Network,Fiber,Reconstruction,Neurons,Vessel,Vascular,confocal,microscopy
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: ~=3.7
Description-Content-Type: text/markdown
Provides-Extra: kimimaro
