Adds get_row function to tasks:

- get_row function structure added to task class
- get_row returns a len(assets)x2 array of zeros for runability of code. This will need to be changed with a real calcualtion of costs and durations
- Irma constructs a task asset matrix by adding up these rows
- The task asset matrix built by irma is rows = tasks, columns = assets

Author: RyanDavies19

famodel/irma/irma.py

@@ -436,8 +436,9 @@ def findCompatibleVessels(self):
 
 
     # ----- Generate tasks (groups of Actions according to specific strategies) -----
-        
+    tasks = []
     t1 = Task(sc.actions, 'install_mooring_system')
+    tasks.append(t1)
 
     # ----- Do some graph analysis -----
 
@@ -455,8 +456,17 @@ def findCompatibleVessels(self):
     for akey, anchor in project.anchorList.items():
         for a in anchor.install_dependencies:  # go through required actions (should just be the anchor install)
             a.evaluateAssets({'carrier' : sc.vessels["MPSV_01"]})  # see if this example vessel can do it
-    
-    
+
+
+    # ----- Generate the task_asset_matrix for scheduler -----
+    # UNUSED FOR NOW
+    task_asset_matrix = np.zeros((len(tasks), len(sc.vessels), 2))
+    for i, task in enumerate(tasks):
+        row = task.get_row(sc.vessels)
+        if row.shape != (len(sc.vessels), 2):
+            raise Exception(f"Task '{task.name}' get_row output has wrong shape {row.shape}, should be {(2, len(sc.vessels))}")
+        task_asset_matrix[i, :] = row
+
     # ----- Call the scheduler -----
     # for timing with weather windows and vessel assignments 
 

famodel/irma/task.py

@@ -92,4 +92,38 @@ def getTaskGraph(self):
         longest_path = nx.dag_longest_path(G, weight='duration')
         longest_path_edges = list(zip(longest_path, longest_path[1:]))  # Convert path into edge pairs
         total_duration = sum(self.actionList[node].duration for node in longest_path)
-        return G  
+        return G  
+    
+    def get_row(self, assets):
+        '''Get a matrix of (cost, duration) tuples for each asset to perform this task. Will be a row in the task_asset matrix.
+        
+        Parameters
+        ----------
+        assets : list
+            A list of all assets available to perform the task.
+        
+        Returns
+        -------
+        matrix : array-like
+            A 2D array of (cost, duration) tuples indicating the cost and duration for each asset to perform this task.
+            Must be 2x len(assets).
+        
+        '''
+
+        matrix = np.zeros((len(assets), 2))
+        # TODO: build row of matrix that holds (cost, duration) tuple of asset / assets (?) to complete task
+
+        # Could look something like...
+        ''' 
+        for i, asset in enumerate(assets):
+            for action in self.actionList: # can we do this without the double loop?
+                if asset in action.roles:
+                    action = self.actionList[asset.name]
+                    matrix[i, 0] = action.cost
+                    matrix[i, 1] = action.duration
+                else:
+                    matrix[i, 0] = -1 # negative cost/duration means asset cannot perform task
+                    matrix[i, 1] = -1
+        '''
+
+        return np.zeros((len(assets), 2)) # placeholder, replace with actual matrix