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Export the synthetic datasets used by all tasks to CSV.
Run from project root: python data/export_data.py
Writes:
data/customers.csv - Task 2 (400 customers, biased urban/rural mix)
data/sales_history.csv - Task 5 (730 days of synthetic daily sales)
data/network_nodes.csv - Tasks 3/4 (20-node delivery network)
data/network_edges.csv - Tasks 3/4 (weighted edges + CO2 cost)
All generators use a fixed seed (42), so re-running this produces identical files.
"""
import os, sys
import pandas as pd
# Allow `python data/export_data.py` from project root
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from task2_segmentation import generate_biased_data
from task5_forecasting import generate_sales
from task3_4_routing import _build_inline_network
OUT = os.path.dirname(os.path.abspath(__file__))
def export_customers():
df = generate_biased_data()
path = os.path.join(OUT, "customers.csv")
df.to_csv(path, index=False)
print(f" customers.csv ({len(df)} rows)")
def export_sales():
df = generate_sales()
path = os.path.join(OUT, "sales_history.csv")
df.to_csv(path, index=False)
print(f" sales_history.csv ({len(df)} rows)")
def export_network():
nodes_dict, edges_list, co2_list = _build_inline_network()
nodes = pd.DataFrame(
[(n, x, y, region) for n, (x, y, region) in nodes_dict.items()],
columns=["node", "x", "y", "region"],
)
nodes.to_csv(os.path.join(OUT, "network_nodes.csv"), index=False)
print(f" network_nodes.csv ({len(nodes)} rows)")
edges = pd.DataFrame(
[(a, b, km, co2) for (a, b, km), (_, _, co2) in zip(edges_list, co2_list)],
columns=["from", "to", "distance_km", "co2_kg"],
)
edges.to_csv(os.path.join(OUT, "network_edges.csv"), index=False)
print(f" network_edges.csv ({len(edges)} rows)")
if __name__ == "__main__":
print("Exporting synthetic datasets to data/:")
export_customers()
export_sales()
export_network()
print("Done.")
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