# Dijkstra’s Algorithm Calculator : A Step-by-Step Guide with Code

If you’re into computer science and algorithms, you’ve likely come across Dijkstra’s Algorithm Calculator. It’s a fundamental algorithm used to find the shortest path between nodes in a graph. In this comprehensive guide, we’ll walk you through Dijkstra’s algorithm, explain how it works, provide step-by-step instructions, and even give you a code example in Python.

## Understanding Dijkstra’s Algorithm

Dijkstra’s algorithm, developed by Dutch computer scientist Edsger W. Dijkstra in 1956, is used to solve the single-source shortest path problem in a weighted, directed graph. It finds the shortest path from a starting node to all other nodes in the graph, ensuring that no negative-weight edges are present.

## Step-by-Step Guide

Let’s break down Dijkstra’s algorithm into simple steps:

• Initialize: Start with a source node and initialize the distance to itself as 0. Set the distance to all other nodes as infinity (or a very large number).
• Visit Neighbors: Visit all the neighbors of the source node and update their distances. The new distance is the sum of the source node’s distance and the weight of the edge to that neighbor. If this new distance is shorter than the current recorded distance, update it.
• Mark Visited: Once you’ve visited all neighbors, mark the current node as visited to prevent revisiting it.
• Select the Next Node: Choose the unvisited node with the shortest distance as the next node to explore. Repeat steps 2-4 for this node.
• Repeat: Keep repeating steps 2-4 until you’ve visited all nodes or until the destination node is reached (if you have one).

## Python Code Example

```import heapq

def dijkstra(graph, start):
distances = {node: float('inf') for node in graph}
distances[start] = 0
priority_queue = [(0, start)]

while priority_queue:
current_distance, current_node = heapq.heappop(priority_queue)

if current_distance &gt; distances[current_node]:
continue

for neighbor, weight in graph[current_node].items():
distance = current_distance + weight

if distance &lt; distances[neighbor]:
distances[neighbor] = distance
heapq.heappush(priority_queue, (distance, neighbor))

return distances
```

Here’s a Python implementation of Dijkstra’s algorithm using a priority queue. You can use this code to calculate the shortest distances from a source node to all other nodes in a graph. Make sure to represent your graph as a dictionary with nodes as keys and their neighbors and edge weights as values.

```import matplotlib.pyplot as plt
import sys

# Helper function to visualize the graph
def visualize_graph(graph):
for node, neighbors in graph.items():
for neighbor, weight in neighbors.items():
plt.plot([node, neighbor], [0, 0], 'bo-')
plt.text(node, 0.02, str(node), ha='center')
plt.text(neighbor, -0.02, str(neighbor), ha='center')
plt.text((node + neighbor) / 2, 0.04, str(weight), ha='center')

plt.ylim(-0.1, 0.1)
plt.show()

# Dijkstra's Algorithm implementation
def dijkstra(graph, start):
shortest_path = {node: float('inf') for node in graph}
shortest_path[start] = 0
visited = set()

while len(visited) < len(graph):
current_node = None
current_min = float('inf')

for node in graph:
if shortest_path[node] < current_min and node not in visited:
current_node = node
current_min = shortest_path[node]

if current_node is None:
break

for neighbor, weight in graph[current_node].items():
potential_path = shortest_path[current_node] + weight

if potential_path < shortest_path[neighbor]:
shortest_path[neighbor] = potential_path

return shortest_path

# Example usage
if __name__ == "__main__":
graph = {
'A': {'B': 1, 'C': 4},
'B': {'A': 1, 'C': 2, 'D': 5},
'C': {'A': 4, 'B': 2, 'D': 1},
'D': {'B': 5, 'C': 1}
}

visualize_graph(graph)

start_node = 'A'
shortest_paths = dijkstra(graph, start_node)

print(f"Shortest paths from node {start_node}:")
for node, distance in shortest_paths.items():
print(f"Node {node}: {distance} units away")
```