Fracture Network Analysis
Project description
fractopo is a Python module that contains tools for validating and analysing lineament and fracture trace maps (fracture networks).
Full Documentation is hosted on Read the Docs:
Installation
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.
Conda
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
Pip
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]
poetry
For usage:
poetry add fractopo
For development:
git clone https://github.com/nialov/fractopo --depth 1
cd fractopo
poetry install
Usage
fractopo has two main use cases:
Validation of lineament & fracture trace data
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 geometrical and topological inconsistencies between the traces.
Input data
Reading and writing spatial filetypes is done in geopandas and you should see geopandas documentation for more advanced read-write use cases:
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 validation
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
Geometric and topological trace network analysis
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
Development
Breaking changes are possible and expected.
Development dependencies for fractopo include:
poetry
# Use poetry run to execute poetry installed cli tools such as invoke, # nox and pytest. poetry run <cmd>
Used to handle Python package dependencies.
invoke
Will be replaced by doit
# To list invoke tasks (replaces Makefile) # Tasks are defined in tasks.py poetry run invoke --list
nox
# To list available nox sessions # Sessions are defined in noxfile.py poetry run nox --list
nox is a replacement for tox. Both are made to create reproducible Python environments for testing, making docs locally, etc.
copier
# To pull copier update from github/nialov/nialov-py-template poetry run copier update
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.
pytest
# To run tests implemented in ./tests directory and as doctests # within project itself: poetry run pytest
pytest is a Python test runner.
coverage
# To check coverage of tests # (Implemented as nox session!) poetry run nox --session test_pip
pytest is a Python test runner.
sphinx
# To create documentation # (Implemented as nox session!) poetry run nox --session docs
Big thanks to all maintainers of the above packages!
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