MGSurvE 0.6.5.0

MGSurvE

MGSurvE: Mosquito Gene SurveillancE

MGSurvE is a project that optimizes mosquito traps' placement in complex heterogeneous landscapes in an effort to minimize the time to detection of genetic variants of interest.

Please have a look at the documentation for more info and our pypi package for detailed installation instructions, and tutorials.

To install the package's latest stable version run:

pip install MGSurvE

The usage of anaconda for environment management is strongly recommended.

MGSurvE requires the installation of the DEAP optimization package, which should be installed automatically with our previous pip command. This package can also be installed with conda install deap, if needed; or by having a look at DEAP's documentation for additional methods,or at our installation instructions for common issues with some of the dependencies!

Additionally, some of MGSurvE's map-plotting functions use cartopy. Even though the dependency's installation is not strictly required, the easiest way to install this package is with conda install cartopy, but in case there are errors in this process, have a look at the package's installation instructions.

Under Construction :construction: Please check back in a couple of weeks!

Features and Development

• Different point-types
• Random point-process landscape generators
• Customizable movement kernels
• Customizable trap attractiveness kernels
• Integrated plotting routines
• GA Optimization routines
• Directed network plotting routines
• Demos and Examples
• Immovable traps
• Different attractiveness levels at specific point-type transitions
• Different movement kernels for male and female mosquitos
• Different trap attractiveness levels for male and female mosquitos
• Thorough timing benchmarks
• Support for xy coordinate systems
• Integrated Map-plotting routines
• Attractiveness/repellency effects

Authors and Funders

• Lead and Dev: Héctor M. Sánchez C.
• Active Devs: Elijah Bartolome, Lillian Weng, Ayden Salazar, Xingli Yu, Joanna Yoo
• PIs: David L. Smith, John M. Marshall