<｜begin▁of▁sentence｜>### Instruction:
In the course of scrutinizing a network of financial transactions laid out on a ledger that metaphorically represents a 3x3 grid matrix, where each transaction point is logged with a corresponding coordinate set (x, y), I seek your expertise in deploying an analytical strategy akin to the A* algorithm to trace the most economical route. This navigation is from the commencement point, the bottom-left of the grid at coordinate (0, 0), proceeding to the conclusive transaction at the upper-right of the matrix, located at (2, 2). The evaluation should employ the Euclidean distance as the heuristic measure for determining the shortest path between the transactions. Could you reformulate the approach to trace this financial route using the NetworkX tools, while treating each step between transactions as a movement along the grid?
### Response:
import networkx as nx

# Create a graph
G = nx.grid_graph(dim=[3, 3])  # nodes are two-tuples (x, y)

# Define a heuristic function
def dist(a, b):
    (x1, y1) = a
    (x2, y2) = b
    return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5

# Use the A* algorithm
path = nx.astar_path(G, (0, 0), (2, 2), heuristic=dist)

print('Path using A*: ', path)
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