<｜begin▁of▁sentence｜>### Instruction:
Imagine you're the Project Scheduler in charge of a construction project and each phase of the project is represented as a space within a building, with 'A', 'B', 'C', and 'D' being different rooms in this building. To plan effectively, you need to create a schedule that relies on the understanding of the time (weight) it takes to transition from one phase (room) to another. 

You have a data report that outlines the connections (edges) between project phases with associated times. The report data includes the following edges and weights: a connection from Room A to Room B with a time cost of 2, Room A to Room C with a time cost of 3, Room B to Room C taking 5, and finally, Room C to Room D requiring 4 time units.

Each phase of the project also has a label that corresponds to the room where it takes place, and these room labels are uppercase versions of the room codes. So, Room A is where phase 'a' occurs, and so on. 

Your task is to map out the project timeline effectively. To do this digitally and ensure optimal resource allocation, how can you translate this report into a SciPy sparse matrix? This matrix should represent the time costs and should be organized by the uppercase room labels, which align with the project phase codes. This way, you could visualize the project plan and make scheduling decisions based on a clear, efficient structure.
### Response:
import networkx as nx
from scipy.sparse import csr_matrix

# Create a graph
G = nx.Graph()
G.add_edge('a', 'b', weight=2)
G.add_edge('a', 'c', weight=3)
G.add_edge('b', 'c', weight=5)
G.add_edge('c', 'd', weight=4)

# Node attribute for each node
for node in G.nodes():
    G.nodes[node]['room'] = node.upper()

# Generate the sparse matrix using 'weight' as edge attribute and 'room' as node attribute
M, nodes_order = nx.attr_sparse_matrix(G, edge_attr='weight', node_attr='room')

print('Nodes order:', nodes_order)
print('Sparse matrix:\n', M.toarray())
<|EOT|>
