Add failure graphs capability in progress

failure/twoTurbineCaseStudy.py

@@ -0,0 +1,76 @@
+import numpy as np
+from failure.graphBuilder import *
+from failure.searchMethods import *
+
+''' twoTurbineCaseStudy.py -----------------------------------------------------------------------------------------------
+
+            ****************************** Last Updated: 18 April 2024 ******************************
+
+ Methods:
+ 1) twoTurbine_bayesian_table: input adjusted adjacency matrix, adjacency matrix, current node, list of node names,
+ adjusted list of node names --> outputs list of parents, probability table
+
+-----------------------------------------------------------------------------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
+
+
+   twoTurbine_bayesian_table Documentation
+ ------------------------------------------
+ This method inputs an adjusted adjacency matrix, regular adjacency matrix, current node (integer), array of node names (strings), 
+ and adjusted node names (same indexing as adjusted adjacency matrix). We then calculate the probability table for the current node
+ given its parents in the adjusted adjacency matrix. We use the weights from the regular adjacency matrix to determine the transitional
+ probabilities between nodes. We output an array of the current node's parents and its probability table.'''
+
+
+def twoTurbine_bayesian_table(a, arr, current, nodeNames, non):
+    # arr, nodeNames = excel2Matrix("ExcelFiles/failureData.xlsx", "twoTurbines_simplified") # For debugging, feel free to uncomment
+    adj = a.copy()
+    nodes = diagonal_nodes(adj) # Diagonal matrix of node's numerical names +1
+    adj = make_binary(adj, 0) # Binarize adjacency matrix
+    parents_bool = nodes @ adj[:, current-1] # vector of zeros and parent names (numerical names)
+    parents = list(parents_bool[np.nonzero(parents_bool)]) # list of just the parent names (numerical names)
+    prob_table = np.zeros((2 ** len(parents), len(parents) + 2)) # Initialize the array for the node's conditional probability distribution
+
+    current_index = np.where(nodeNames == non[int(current - 1)])[0][0]
+
+    for iteration in range(2 ** len(parents)): # For each combination of parents having either failed or not
+        true_false_array = [] # Iniitalize to record which parents have failed
+
+        for p in range(len(parents)):
+            parent_index = np.where(nodeNames == non[int(parents[p] - 1)])[0][0]
+            prob_table[iteration][p] = int(iteration / (2 ** p)) % 2 # Append whether or not the parent node has failed
+            prob_table[iteration][-2] += (1- int(iteration / (2 ** p)) % 2) * arr[parent_index][current_index] # Determine parent probability (given if the parent has failed or not)
+            true_false_array.append((int(iteration / (2 ** p)) % 2)) # Append whether or not the parent node has failed
+
+        if prob_table[iteration][-2] > 1: # If the value is greater than 1, set the probability to 1
+            prob_table[iteration][-2]  = 1
+        prob_table[iteration][-1] = 1 - prob_table[iteration][-2] # Add the probability of the node not failing to the array
+    # print(prob_table)
+    return parents, prob_table
+
+
+
+'''
+# The following code is for writing excel file with all pair-wise probabilities. To run, copy and paste the code below into main.py and hit 'Run.'
+
+adjacency_matrix, nodeNames = excel2Matrix("ExcelFiles/failureData.xlsx", "twoTurbines_simplified")
+current = 1
+midpoint = True
+num_iterations = 1
+probabilities, nodeNames = getProbabilities("ExcelFiles/failureProbabilities.xlsx", sheetName = "Conditional Probabilities (2)")
+all_probs = np.zeros(adjacency_matrix.shape)
+with pd.ExcelWriter("bayesian_simulations3.xlsx") as writer:
+    for i in range(1, len(nodeNames) + 1):
+        print("----------------------------------", i, "----------------------------------")
+        array_of_probs = np.zeros((len(nodeNames), 2))
+        for j in range(1,len(nodeNames)+1):
+            adjacency_matrix2 = adjacency_matrix.copy()
+            probabilities = np.array(probabilities).copy()
+            A, B = backward_bayesian_inference(adjacency_matrix2, nodeNames, [i], [i], [j], probabilities, start_bool = True, multi = False, poc="child", twoTurbine = True)
+            array_of_probs[j-1] = A
+        df = pd.DataFrame(np.array(array_of_probs))
+        df.to_excel(writer, sheet_name="Probabilities given "+str(i))
+        all_probs[:,i-1] = array_of_probs[:,0]
+    df2 = pd.DataFrame(np.array(all_probs))
+    df2.to_excel(writer, sheet_name="allProbs")
+'''