fractopo is a Python module that contains tools for validating and analysing lineament and fracture trace maps (fracture networks).

Overview of fractopo

• Full Documentation is hosted on Read the Docs:
  • Documentation

pip and poetry installation only supported for linux and MacOS based operating systems. For Windows install using (ana)conda.

For pip and poetry: Omit --dev or [dev] for regular installation. Keep if you want to test/develop or otherwise install all development python dependencies.

• Only supported installation method for Windows!

# Create new environment for fractopo (recommended)
conda env create fractopo-env
conda activate fractopo-env
# Available on conda-forge channel
conda install -c conda-forge fractopo

The module is on PyPI.

# Non-development installation
pip install fractopo

Or locally for development:

git clone https://github.com/nialov/fractopo
cd fractopo
# Omit [dev] from end if you do not want installation for development
pip install --editable .[dev]

For usage:

poetry add fractopo

For development:

git clone https://github.com/nialov/fractopo --depth 1
cd fractopo
poetry install

fractopo has two main use cases:

1. Validation of lineament & fracture trace data
2. Analysis of lineament & fracture trace data

Validation is done to make sure the data is valid for the analysis and is crucial as analysis cannot take into account different kinds of geometric and topological inconsistencies between the traces.

Reading and writing spatial filetypes is done in geopandas and you should see geopandas documentation for more advanced read-write use cases:

• https://geopandas.org/

Simple example with trace and area data in GeoPackages:

import geopandas as gpd

# Trace data is in a file `traces.gpkg` in current working directory
# Area data is in a file `areas.gpkg` in current working directory
trace_data = gpd.read_file("traces.gpkg")
area_data = gpd.read_file("areas.gpkg")

Trace and target area data can be validated for further analysis with a Validation object.

from fractopo import Validation

validation = Validation(
    trace_data,
    area_data,
    name="mytraces",
    allow_fix=True,
)

# Validation is done explicitly with `run_validation` method
validated_trace_data = validation.run_validation()

Trace validation is also accessible as a command-line script, fractopo tracevalidate which is more straightforward to use than through Python calls. Note that all subcommands of fractopo are available by appending them after fractopo.

tracevalidate always requires the target area that delineates trace data.

# Get full up-to-date script help

fractopo tracevalidate --help

# Basic usage example:

fractopo tracevalidate /path/to/trace_data.shp /path/to/target_area.shp\
   --output /path/to/validated_trace_data.shp

# Or with automatic saving to validated/ directory

fractopo tracevalidate /path/to/trace_data.shp /path/to/target_area.shp\
   --summary

Trace and target area data (GeoDataFrames) are passed into a Network object which has properties and functions for returning and visualizing different parameters and attributes of trace data.

from fractopo import Network

# Initialize Network object and determine the topological branches and nodes
network = Network(
    trace_data,
    area_data,
    # Give the Network a name!
    name="mynetwork",
    # Specify whether to determine topological branches and nodes
    # (Required for almost all analysis)
    determine_branches_nodes=True,
    # Specify the snapping distance threshold to define when traces are
    # snapped to each other
    snap_threshold=0.001,
    # If the target area used in digitization is a circle, the knowledge can
    # be used in some analysis
    circular_target_area=True,
    # Analysis on traces can be done for the full inputted dataset or the
    # traces can be cropped to the target area before analysis (cropping
    # recommended)
    truncate_traces=True,
)

# Properties are easily accessible
# e.g.
network.branch_counts
network.node_counts

# Plotting is done by plot_ -prefixed methods
network.plot_trace_lengths()

Network analysis is also available as a command-line script but using the Python interface (e.g. jupyter lab, ipython) is recommended when analysing Networks to have access to all available analysis and plotting methods. The command-line entrypoint is opinionated in what outputs it produces. Brief example of command-line entrypoint:

fractopo network /path/to/trace_data.shp /path/to/area_data.shp\
   --name mynetwork

# Use --help to see all up-to-date arguments and help
fractopo network --help

See full documentation for more examples and help:

• https://fractopo.readthedocs.io/en/latest/index.html#full-documentation

To cite this software:

• The software is introduced in https://doi.org/10.1016/j.jsg.2022.104528 and you can cite that article as a general citation:

Ovaskainen, N., Nordbäck, N., Skyttä, P. and Engström, J., 2022. A new
subsampling methodology to optimize the characterization of
two-dimensional bedrock fracture networks. Journal of Structural Geology,
p.104528.

• To cite a specific version of fractopo you can use a zenodo provided doi. E.g. https://doi.org/10.5281/zenodo.5957206 for version v0.2.6. See the zenodo page of fractopo for the doi of each version: https://doi.org/10.5281/zenodo.5517485

• Breaking changes are possible and expected.

Development dependencies for fractopo include:

• poetry

  • Used to handle Python package dependencies.

  # Use poetry run to execute poetry installed cli tools such as invoke,
  # nox and pytest.
  poetry run <cmd>

• doit

  • A general task executor that is a replacement for a Makefile
  • Understands task dependencies and can run tasks in parallel even while running them in the order determined from dependencies between tasks. E.g. requirements.txt is a requirement for running tests and therefore the task creating requirements.txt will always run before the test task.

  # Tasks are defined in dodo.py
  # To list doit tasks from command line
  poetry run doit list
  # To run all tasks in parallel (recommended before pushing and/or
  # committing)
  # 8 is the number of cpu cores, change as wanted
  # -v 0 sets verbosity to very low. (Errors will always still be printed.)
  poetry run doit -n 8 -v 0

• nox

  • nox is a replacement for tox. Both are made to create reproducible Python environments for testing, making docs locally, etc.

  # To list available nox sessions
  # Sessions are defined in noxfile.py
  poetry run nox --list

• copier

  • copier is a project templater. Many Python projects follow a similar framework for testing, creating documentations and overall placement of files and configuration. copier allows creating a template project (e.g. https://github.com/nialov/nialov-py-template) which can be firstly cloned as the framework for your own package and secondly to pull updates from the template to your already started project.

  # To pull copier update from github/nialov/nialov-py-template
  poetry run copier update

• pytest

  • pytest is a Python test runner. It is used to run defined tests to check that the package executes as expected. The defined tests in ./tests contain many regression tests (done with pytest-regressions) that make it almost impossible to add features to fractopo that changes the results of functions and methods.

  # To run tests implemented in ./tests directory and as doctests
  # within project itself:
  poetry run pytest

• coverage

  # To check coverage of tests
  # (Implemented as nox session!)
  poetry run nox --session test_pip

• sphinx

  • Creates documentation from files in ./docs_src.

  # To create documentation
  # (Implemented as nox session!)
  poetry run nox --session docs

Big thanks to all maintainers of the above packages!

Copyright © 2020, Nikolas Ovaskainen.