Zakaria
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
# Student ID: 012498637
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# Student Name: Zakaria Benmoulay
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# Course: C950 - Data Structures and Algorithms II
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import csv
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from package import Package
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from hash_table import ChainingHashTable
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def load_packages(filepath):
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# Let's read packages.csv and load all package records into a hash table.
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table = ChainingHashTable()
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with open(filepath, newline="", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for row in reader:
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# Build a Package object from each CSV row
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pkg = Package(
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package_id=row["PackageID"],
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address=row["Address"],
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city=row["City"],
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state=row["State"],
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zip_code=row["Zip"],
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deadline=row["Deadline"],
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weight=row["Weight"],
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notes=row["Notes"],
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)
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# Insert into the hash table using package ID as the key
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table.insert(pkg.package_id, pkg)
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# Let's return the hash table keyed by package ID
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return table
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def load_distances(filepath):
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# Let's read distances.csv and build a symmetric 2-D distance matrix.
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address_list = []
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raw_rows = []
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with open(filepath, newline="", encoding="utf-8") as f:
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reader = csv.reader(f)
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# First row contains the column headers (location name + address)
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header = next(reader)
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# Extract the short street address from each header cell.
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# Header cells may contain a full location name followed by the
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# street address separated by whitespace; we take the last part.
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for cell in header[1:]: # skip the first "Address" label column
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parts = [p.strip() for p in cell.replace("\n", " ").split(" ") if p.strip()]
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address_list.append(parts[-1] if parts else cell.strip())
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# Read the remaining rows — each row is one location's distances
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for row in reader:
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raw_rows.append(row[1:]) # drop the first column (location label)
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m = len(address_list)
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# Create an m×m matrix filled with 0.0
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matrix = [[0.0] * m for _ in range(m)]
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# Fill in the matrix - the CSV only has the lower triangle
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for i, row in enumerate(raw_rows):
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for j, cell in enumerate(row):
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if cell.strip() == "" or cell is None:
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continue
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dist = float(cell)
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matrix[i][j] = dist
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matrix[j][i] = dist
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return address_list, matrix
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def get_distance(address_list, distance_matrix, addr_a, addr_b):
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# Let's find the index of an address using flexible matching.
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def find_index(addr):
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addr_clean = addr.strip().lower()
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# Pass 1: exact match (fastest, covers most cases)
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for i, a in enumerate(address_list):
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if a.strip().lower() == addr_clean:
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return i
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# Pass 2: substring match (handles minor formatting differences)
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for i, a in enumerate(address_list):
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if addr_clean in a.strip().lower() or a.strip().lower() in addr_clean:
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return i
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# Raise an error if the address cannot be found.
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raise ValueError(f"Address not found in distance table: '{addr}'")
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i = find_index(addr_a)
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j = find_index(addr_b)
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return distance_matrix[i][j]
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