Converge.py Inputs and Arguments

The converge.py program takes the data generated from run.py and provides the user with information about simulation progress, including the lengths of simulations, the number of transitions (bounces) against milestones, and convergence information.

In addition, the run.py program can be configured to use the output of converge.py’s API to run simulations to convergence.

usage:

python converge.py [-h] [-s K_ON_STATE] [-d IMAGE_DIRECTORY] [-c CUTOFF]
                 [-m MINIMUM_ANCHOR_TRANSITIONS] [-p] MODEL_FILE

Required Arguments

MODEL_FILE: Provide a path to a file named “model.xml” which can be found in the <root_directory> tag of the model input file provided to prepare.py.

Optional Arguments

-h, --help: show help message and exit.
-s K_ON_STATE, --k_on_state K_ON_STATE: Define the bound state (anchor index) that will be used to compute k-on. If left blank, and k-on statistics exist, then the first end state in the model will be chosen by default.
-d IMAGE_DIRECTORY, --image_directory IMAGE_DIRECTORY: Define the directory where all plots and images will be saved. By default, graphics will be saved to the ‘images_and_plots/’ directory in the model’s anchor root directory.
-c CUTOFF, --cutoff CUTOFF: The minimum convergence that must be achieved before concluding that the calculations have converged for a given anchor.
-m MINIMUM_ANCHOR_TRANSITIONS, --minimum_anchor_transitions MINIMUM_ANCHOR_TRANSITIONS: Enter a minimum number of transitions that must be observed per milestone in a given MD anchor as a criteria for the simulations to be considered converged.