Using the Analysis helper class¶

After setting up your project directory with smartmove.create_project(), a template YAML files were copied to your project directory that configure the information about the project, experiment indices, glide identification parameters, sub-glide filtering parameters, and parameters for the ANN. Read more about the configuration files here in Configuration files.

Once your project directory is configured, you can use the Analysis helper class for running different parts of the analysis. The Analysis class object keeps track of the configuration files and the ANN analysis from which to generate tables and figures from. This allows you to easily run new ANN configurations at a later time and inspect the results of all models that have been run.

Create an analysis¶

Activate your virtual environment, and then lauch a python interpreter.

cd ~/opt/smartmove/
source venv/bin/activate
Ipython3

Then initiate an Analysis class object from which to run and inpect results from the ANN. After initializing your analysis, you can execute the glide identification function with the class method run_glides(), which will walk you through the glide identification.

import smartmove

path_project = './'

a = smartmove.Analysis(path_project)

You can inspect the attributes of the object from within a Python interpreter, such as iPython:

# Show the names of attributes for `a`
vars(a).keys()

# Show all attributes and class methods available from `a`
dir(a)

# Print the glide configuration dictionary
a.cfg_glide

Glide identification¶

# Run with `cfg_glide` and splitting sub-glides into 2s segments
a.run_glides(sgl_dur=2)

See the Glide identification documentation for an overview of the procedure.

Run ANN¶

a.run_ann()

See the Artifical Neural Network Analysis documentation for an overview of the procedure.

Paper figures and tables¶

Running the following routine will create a paper/ subdirectory the project directory passed when initializing your Analysis class object.

Be sure to load the ANN analysis you wish to produce figures for before running the commands.

select

# Generate figures
a.make_tables()

# Generate figures
a.make_figures()