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Tools to analyze scalar and vector fields

Project description

Build status Overall test coverage Documentation Status

This module has been written to carry out analysis and more specifically structure detection on PIV velocity fields. It is now more general and can handle different kind of data (point cloud, scalar and vector field, …) and perform classical and more advanced analysis on them (spectra, pod, post-processing, visualization, …).

Hosted on FramaGit.

Full documentation available on ReadTheDocs.

General data analysis

  1. Class representing 2D fields of 1 component (ScalarField)
  2. Class representing 2D fields of 2 components (VectorField)
  3. Classes representing sets of scalar fields vector fields (SpatialScalarFields, TemporalScalarFields, SpatialVectorFields and TemporalVectorFields)
  4. Class representing profiles (Profile)
  5. Class representing scatter points (Points)
  6. Module for modal decomposition (POD, DMD) and reconstruction (pod)
  7. Module to import/export data from/to Davis, matlab, ascii, pivmat and images files (file_operation)
  8. Functionalities to visualize those data (plotlib)

Flow analysis

  1. Module to create artificial vortices: Burger, Jill, Rankine, … and to simulate their motion in potential flows (vortex_creation)
  2. Module providing several vortex criterions computation (vortex_criterions)
  3. Module to automatically detect and track critical points (vortex_detection)
  4. Module to compute the evolution of some vortex properties (vortex_properties)
  5. Module to generate potential flows with arbitrary geometries (potential_flow)

Dependencies

Mandatory:

  • numpy
  • matplotlib
  • scipy
  • unum
  • modred

Optional:

  • sklearn (to work with point clustering)
  • networkx (to use force-directed algorithms to compare trajectories)
  • colorama (to have a nice interface when manipulating files)
  • h5py (allow to import data from pivmat files)

Installation

IMTreatment is now available on Pypi: pip install IMTreatment

Or if you prefer to install it manually from source: pip install 'git+https://framagit.org/gabylaunay/IMTreatment.git#egg=IMTreatment'

Documentation

IMTreatment is documented inline and in ReadTheDocs. you can also use build_doc.sh to locally build the html doc.

Project details


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