<｜begin▁of▁sentence｜>### Instruction:
As a health coach guiding clients through the intricate journey of lifestyle transformation, imagine we have a roadmap laid out with various checkpoints. Think of this roadmap as a flowchart of healthy habits, where each habit leads naturally to another, creating a path to wellness without any loops or backward steps—essentially, a "Directed Acyclic Graph" of well-being.

Now, to simplify our conversation and make tracking progress more manageable, let's envisage each checkpoint as a unique step in our health regimen. Currently, on our chart, some steps branch out to multiple subsequent steps. Consider the path where starting from the initial step of mindful eating (1), it branches out to both consistent exercise (2) and adequate hydration (3). Maintaining hydration (3) is directly linked to getting enough sleep (4), just as consistent exercise (2) is.

Our goal is to create a more straightforward, single-strand pathway from each origin point to ensure clarity in our health journey, essentially a "branching" of this flowchart. In the NetworkX language, we would represent the connections as a series of relationships like [(1, 2), (1, 3), (3, 4), (2, 4)].

Could you transform our current wellness roadmap into a simplified "branching" format using NetworkX? Once transformed, I'd like to see the clear, linear connections between steps. Additionally, to maintain a reference to our original plan, it would be very helpful if you could provide a list that shows which new, individual step corresponds to each step in our initial, comprehensive regimen.

By doing this, we can ensure that anyone following the roadmap can see a clear, direct line from each habit to the next, making the lifestyle change process as straightforward and manageable as possible.
### Response:
import networkx as nx

# Create a directed acyclic graph (DAG)
G = nx.DiGraph([(1, 2), (1, 3), (3, 4), (2, 4)])

# Convert the DAG to a branching
B = nx.dag_to_branching(G)

# Now let's print the branching
for edge in B.edges():
    print(edge)

# To see the source mapping
source_map = {v: data['source'] for v, data in B.nodes(data=True)}
print(source_map)
<|EOT|>
