Automated quantification of fibrous networks
pip install Qiber3D
You can install the latest version
pip install -U git+https://github.com/theia-dev/Qiber3D.git#egg=Qiber3D
or the dev version directly from GitHub.
pip install -U git+https://github.com/theia-dev/Qiber3D.git@dev#egg=Qiber3D
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 or use the Example class.
import logging
from Qiber3D import Network, config
from Qiber3D.helper import Example, change_log_level
config.extract.nd2_channel_name = 'FITC'
change_log_level(logging.DEBUG)
net_ex = Example.nd2()
net = Network.load(net_ex)
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 more extensive interactive example is available as a Jupyter notebook. You can try it out directly on Binder. More in-depth documentation, including details on the inner working, can be found at Read the docs.
The complete source code is available on GitHub.