An Interpretive Structural Modelling approach for Building information modelling adoption.

This class represents a directed graph and is used to calculate the transitive closure of the given graph using the Floyd-Warshall algorithm. The transitive_closure method computes the transitive closure of the graph using a nested loop structure and returns the reachability matrix.

print_final_reachability(initial, final): This function prints the final reachability matrix, indicating whether each pair of nodes has a reachability path between them. level_partioning(final): This function generates a common matrix that shows the direct reachability relationships for each node. stop_crit(level): This function checks whether the given level array contains any zero, which is used as a stopping criterion in a loop. x_and_y(final): This function calculates the driving power and dependence power for each node in the graph based on the reachability matrix. find_level(intersection_set, common_mat): This function calculates the levels of variables (risks) based on the intersection of reachability sets.

The plot_it(driving_power, dependence_power) function creates a scatter plot of dependence power vs. driving power for each node (risk). It uses Matplotlib to visualize the data points and relationships among variables.

The code prompts the user for the dimension of the reachability matrix and the name of the input file. It loads the input file data into a matrix and then calculates the transitive closure of the graph using the Graph class. The driving power and dependence power for each node are calculated using the x_and_y function. The common matrix and intersection set are computed for level partitioning. The levels of variables are calculated using the find_level function. The plot_it function is called to visualize the relationships among variables using a scatter plot. Finally, a PrettyTable is created to display the levels of each variable in tabular form.