Dynamics Prediction

Added dynamics prediction/simulation functionality. It can now predict two-point and one-point mean square displacement given the connectivity matrix. A new Dynamics class is added to run simulations given the connectivity matrix.

v1.25

For the purpose of obtaining a DOI from Zenodo

v1.21

Add additional option -k or --connectivity-matrix. This option accept the path to the file of connectivity matrix. The file must be text-based file. When specified, the program read load the file and use it as the initialization of the connectivity matrix. Useful if you want to restart the optimization using the result from the previous run.

v1.2

Updates:

• Now you can use --enforece-nonnegative-connectivity-matrix option to enforce that the connectivity matrix remains nonnegative, meaning that all the "spring constants" are either zero or positive. Turning on this option typically result in worse "fitting" to the data. However, it has an advantage if you want to do perturbation such as deletion, inversion, etc on the result connectivity matrix because it won't lead to a non-valid connectivity matrix (being not negative semi-definite)
• Now you can use -m DI to enable the direct inversion method for "fitting" to the data. This will only work if the target distance matrix is a Euclidean matrix or close to a Euclidean matrix. If the distance matrix is computed directly from imaging's (x,y,z) data, this method will naturally work. If the input is the contact map instead, typically this method won't work because the inferred target distance map is often very non-Euclidean. When it does work, it still has one drawback which is that the connectivity matrix obtained is not regularized. Its L2 norm can be very large. Depending on the context, this may or may not be an issue.

v1.1

1. Support Iterative Scaling and Gradient Descent as the optimization method
2. Support L1 and L2 regularization
3. Display more and useful information during the run