Postprocessing

Python scripts for generating flow and concentration fields

In visualization panel the nodes and edges data are used to create the graph topology. We have used Plotly.js to visualize the topological information. We fetch the data and store the co-ordinates of ‘x’, ‘y’, and ‘z’ dimensions to generate the network.

In the context of pressure, velocity and concentration, the graph topology can be used to show how the static and dynamic fields are distributed throughout the system. Nodes can represent pressure points, and edges can represent flow of blood (indicated by velocity vectors) through the microvascular system. There is also a scroller present in the main panel to change in concentration with time.

On the sub-panel a time series graph is represented, displaying the evolution of concentration in the network over time. The horizontal axis of the graph denotes time and a vertical axis denotes concentration. The time series graph animates and updates in real time showing the changing patterns of connections between nodes over time. This provides a visual representation of how the network is evolving and enables users to identify any emerging trends or patterns. The time series graph also includes various interactive features that allow users to manipulate the display such as zooming in and out to focus on specific time periods or nodes of interest.

Output files:

File naming convention.

  • input.xlsx : blood vessel topology and biophysical attributes

  • input.ini : simulation settings (user-defined transport and reaction parameters)

  • Graph.mat : input topology

  • H.mat : input topology discretized

  • Graph_nodes.csv : input topology nodes and its attributes

  • Graph_edges.csv : input topology edges and its attributes

  • H_nodes.csv : discretized topology nodes and its attributes

  • H_edges.csv : discretized topology edges and its attributes

  • results.mat : simulation results with fields pressure, velocity, concentration

  • results_dynamic.json : processed pressure / velocity predictions, loaded to map on graph topology for analysis

  • results_dynamic.json : processed concentration predictions, loaded to map on graph topology for analysis

  • ad (advection + diffusion) or adr (advection + diffusion + reaction)

species = glc_ext/lac_ext/glc_cell/lac_cell

The visualization and analysis can be done independently via the examples shown in process_results.ipynb.