Dynamic Utilities
A module focused on functions and constructors for dynamic graph data.
Warning
Currently under development and constantly evolving. Use with caution.
CofluctTransform
Class for calculating time series co-activation. Based on Esfahlani et al, 2020.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
quantiles |
tuple
|
lower and upper bound of connection strength quantile to look at. |
(0, 1)
|
return_mat |
bool
|
Whether to return matrix (True) or vector (False). |
True
|
adjacency_axis |
int
|
position of the adjacency matrix, default being zero |
0
|
Example
transformer = CofluctTransform(quantiles=(0.95, 1), return_mat=True)
Cofluctuation function (cofluct)
Computes cofluctuation time-series (per edge) for a nodes x timepoints matrix X. Based on https://www.pnas.org/content/early/2020/10/21/2005531117
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
quantiles |
tuple
|
list of lowest/highest quantile of edge events to use [0, 1]: all events = pearson corr; [0, .05]: bottom 5% of events; [.95, 1]: top 5% of events |
(0, 1)
|
return_mat |
Whether to return a connectivity matrix (True) or dictionary (False). The dict edge contains cofluctuation time-series (pairs_of_nodes x timepoints) and event timeseries. |
True
|
Returns:
| Type | Description |
|---|---|
float
|
edge cofluctuation time-series dict (pairs_of_nodes x timepoints) and event timeseries as dict |