<div align="center">
[](https://github.com/gbene/FracAbility/releases/)
[](#license)
[](https://github.com/gbene/FracAbility/issues)
[](https://python.org "Go to Python homepage")
</div>
**FracAbility** is a Python toolbox that can be used to analyse fracture networks in digitalized rock
outcrops. This package provides tools to:
1. Define the topology of fracture networks
2. Statistically analyze fracture length distributions while taking into consideration
right censoring effects ([survival analysis](https://en.wikipedia.org/wiki/Survival_analysis)).
The name Frac**Ability** recalls the [reliability](https://github.com/MatthewReid854/reliability/tree/master)<sup>1</sup>
library that inspired and helped in the creation of this project.
## Quick introduction ⚡
Fracture networks are essential for the analysis and modelling of mechanical and hydraulic properties
of rock masses. Recently, the use of Digital Outcrop Models (**DOMs**) provided a solid framework for the collection
of large and quantitative datasets from which different properties can be extracted.
Because of the complex nature of exposed rock outcrops, statistical model fitting can sometimes be challenging.
Areas covered by rock debree, vegetation patches or simply the outer boundaries of the outcrop can
introduce right-censoring bias and can often lead to parameter underestimation.
The following diagram represents an idealized rock outcrop. We can define the wider rectangle as the entire
fractured object while the smaller one as the outcrop that we can see and measure. We can immediately
see what is going wrong; many of the fractures that we can measure are incomplete thus leading to underestimate
fracture length.
Tools are needed to correct for this bias. Survival analysis techniques, although usually applied
in function of time and not space, accomplishes exactly this.
## Features 📋
- **Shapefile importing support**
- **Rapid topology analysis and identification of I,Y,X and U nodes**
- **Backbone(s) identification**
- **Statistical analysis tools:**
+ Empirical CDF and SF calculation
+ Distribution fitting
+ Statistical model testing
- **Plotting tools:**
+ Network objects plotting using matplotlib or vtk
+ Ternary node plot
+ Rose diagram
+ Statistical plotting
## Installation 🔧
To install fracability pip can be used:
```bash
pip install fracability
```
## Documentation
For usage details please refer to the documentation:
[](https://fracability.readthedocs.io/en/latest/index.html "Go to online documentation")
[](/docs/ "Go to project documentation")
## References 🎓
1. Reid, M. (2020). MatthewReid854/reliability: v0. 5.1. version v0, 5.
## License
Released under [AGPL-3.0](/LICENSE) by [@gbene](https://github.com/gbene)
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"description": "\n<div align=\"center\">\n\n\n\n[](https://github.com/gbene/FracAbility/releases/)\n[](#license)\n[](https://github.com/gbene/FracAbility/issues)\n[](https://python.org \"Go to Python homepage\")\n\n</div>\n\n**FracAbility** is a Python toolbox that can be used to analyse fracture networks in digitalized rock\noutcrops. This package provides tools to:\n\n1. Define the topology of fracture networks \n2. Statistically analyze fracture length distributions while taking into consideration \nright censoring effects ([survival analysis](https://en.wikipedia.org/wiki/Survival_analysis)). \n\nThe name Frac**Ability** recalls the [reliability](https://github.com/MatthewReid854/reliability/tree/master)<sup>1</sup> \nlibrary that inspired and helped in the creation of this project. \n\n\n## Quick introduction \u26a1\n\nFracture networks are essential for the analysis and modelling of mechanical and hydraulic properties \nof rock masses. Recently, the use of Digital Outcrop Models (**DOMs**) provided a solid framework for the collection \nof large and quantitative datasets from which different properties can be extracted.\nBecause of the complex nature of exposed rock outcrops, statistical model fitting can sometimes be challenging. \nAreas covered by rock debree, vegetation patches or simply the outer boundaries of the outcrop can \nintroduce right-censoring bias and can often lead to parameter underestimation.\n\nThe following diagram represents an idealized rock outcrop. We can define the wider rectangle as the entire \nfractured object while the smaller one as the outcrop that we can see and measure. We can immediately \nsee what is going wrong; many of the fractures that we can measure are incomplete thus leading to underestimate \nfracture length. \n\n\n\nTools are needed to correct for this bias. Survival analysis techniques, although usually applied \nin function of time and not space, accomplishes exactly this.\n\n## Features \ud83d\udccb\n\n- **Shapefile importing support**\n\n\n- **Rapid topology analysis and identification of I,Y,X and U nodes**\n\n\n- **Backbone(s) identification**\n\n\n- **Statistical analysis tools:**\n + Empirical CDF and SF calculation\n + Distribution fitting\n + Statistical model testing\n\n\n- **Plotting tools:**\n + Network objects plotting using matplotlib or vtk\n + Ternary node plot \n + Rose diagram\n + Statistical plotting\n\n## Installation \ud83d\udd27\n\nTo install fracability pip can be used:\n\n```bash\npip install fracability\n```\n\n## Documentation\n\nFor usage details please refer to the documentation:\n\n[](https://fracability.readthedocs.io/en/latest/index.html \"Go to online documentation\")\n\n[](/docs/ \"Go to project documentation\")\n\n\n\n## References \ud83c\udf93\n\n1. Reid, M. (2020). MatthewReid854/reliability: v0. 5.1. version v0, 5.\n\n\n## License\n\nReleased under [AGPL-3.0](/LICENSE) by [@gbene](https://github.com/gbene)\n",
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