def test_no_index(self):
if pd is None:
self.skipTest("pandas not available")
df = DataFrame([], ["x", "y"])
x = [1, 2, 3]
y = [4, 5, 6]
df.set_column("x", x)
df.set_column("y", y)
with self.assertRaises(ValueError):
df.to_dict()
pd_df = df.to_pandas()
self.assertEqual(list(pd_df["x"]), x)
self.assertEqual(list(pd_df["y"]), y)
def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
# Create an AMPL instance
ampl = AMPL()
"""
# If the AMPL installation directory is not in the system search path:
from amplpy import Environment
ampl = AMPL(
Environment('full path to the AMPL installation directory'))
"""
ampl.eval("set CITIES; set LINKS within (CITIES cross CITIES);")
ampl.eval("param cost {LINKS} >= 0; param capacity {LINKS} >= 0;")
ampl.eval("data; set CITIES := PITT NE SE BOS EWR BWI ATL MCO;")
cost = [2.5, 3.5, 1.7, 0.7, 1.3, 1.3, 0.8, 0.2, 2.1]
capacity = [250, 250, 100, 100, 100, 100, 100, 100, 100]
links_from = ["PITT", "PITT", "NE", "NE", "NE", "SE", "SE", "SE", "SE"]
links_to = ["NE", "SE", "BOS", "EWR", "BWI", "EWR", "BWI", "ATL", "MCO"]
# Using amplpy.DataFrame
df = DataFrame(("LINKSFrom", "LINKSTo"), ("cost", "capacity"))
df.set_column("LINKSFrom", links_from)
df.set_column("LINKSTo", links_to)
df.set_column("cost", cost)
df.set_column("capacity", capacity)
print(df)
ampl.set_data(df, "LINKS")
ampl.display("LINKS")
# Using pandas.DataFrame (recommended)
df = pd.DataFrame(
list(zip(links_from, links_to, cost, capacity)),
columns=["LINKSFrom", "LINKSTo", "cost", "capacity"],
).set_index(["LINKSFrom", "LINKSTo"])
print(df)
ampl.eval("reset data LINKS;")
ampl.set_data(df, "LINKS")
ampl.display("LINKS")
def test_dataframe(self):
# Create first dataframe (for data indexed over NUTR)
# Add data row by row
df1 = DataFrame("NUTR", ("n_min", "n_max"))
df1.add_row(("A", 700, 20000))
df1.add_row(("B1", 700, 20000))
df1.add_row(("B2", 700, 20000))
df1.add_row(("C", 700, 20000))
df1.add_row(("CAL", 16000, 24000))
df1.add_row(("NA", 0.0, 50000))
# Create second dataframe (for data indexed over FOOD)
# Add column by column
df2 = DataFrame("FOOD")
foods = ["BEEF", "CHK", "FISH", "HAM", "MCH", "MTL", "SPG", "TUR"]
df2.set_column("FOOD", foods)
self.assertEqual(list(df2.get_column("FOOD")), foods)
contents = [2] * 8
df2.add_column("f_min", contents)
self.assertEqual(list(df2.get_column("f_min")), contents)
contents = [10] * 8
df2.add_column("f_max", contents)
self.assertEqual(list(df2.get_column("f_max")), contents)
costs = [3.19, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
df2.add_column("cost", costs)
self.assertEqual(list(df2.get_column("cost")), costs)
labels = [random.choice(string.ascii_letters)] * 8
df2.add_column("labels", labels)
self.assertEqual(list(df2.get_column("labels")), labels)
df2.add_column("empty", [])
self.assertEqual(list(df2.get_column("empty")), [None] * 8)
print(df2.get_column("FOOD"))
for index in df2.get_column("FOOD"):
print(df2.get_row(index))
# Create third dataframe, to assign data to the AMPL entity
# param amt{NUTR, FOOD};
df3 = DataFrame(("NUTR", "FOOD"))
# Populate the set columns
nutr_with_multiplicity = [""] * 48
food_with_multiplicity = [""] * 48
i = 0
for n in range(6):
for f in range(8):
print(df1.get_row_by_index(n)[0])
nutr_with_multiplicity[i] = df1.get_row_by_index(n)[0]
food_with_multiplicity[i] = foods[f]
i += 1
df3.set_column("NUTR", nutr_with_multiplicity)
df3.set_column("FOOD", food_with_multiplicity)
# Populate with all these values
values = [
60,
8,
8,
40,
15,
70,
25,
60,
10,
20,
15,
35,
15,
15,
25,
15,
15,
20,
10,
10,
15,
15,
15,
10,
20,
0,
10,
40,
35,
30,
50,
20,
295,
770,
440,
430,
315,
400,
370,
450,
968,
2180,
945,
278,
1182,
896,
1329,
1397,
]
df3.add_column("amt", values)
def main(argc, argv):
# You can install amplpy with "python -m pip install amplpy"
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
# Note: If you want to perform data transformations use pandas dataframes.
# amplpy dataframes are simple dataframes for data communication only.
# Create first dataframe (for data indexed over NUTR)
# Add data row by row
df1 = DataFrame("NUTR", ("n_min", "n_max"))
df1.add_row("A", 700, 20000)
df1.add_row("B1", 700, 20000)
df1.add_row("B2", 700, 20000)
df1.add_row("C", 700, 20000)
df1.add_row("CAL", 16000, 24000)
df1.add_row("NA", 0.0, 50000)
# Create second dataframe (for data indexed over FOOD)
# Add column by column
df2 = DataFrame("FOOD")
foods = ["BEEF", "CHK", "FISH", "HAM", "MCH", "MTL", "SPG", "TUR"]
df2.set_column("FOOD", foods)
contents = [2] * 8
df2.add_column("f_min", contents)
contents = [10] * 8
df2.add_column("f_max", contents)
costs = [3.19, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
df2.add_column("cost", costs)
# Create third dataframe, to assign data to the AMPL entity
# param amt{NUTR, FOOD};
df3 = DataFrame(index=("NUTR", "FOOD"))
# Populate the set columns
nutr_with_multiplicity = [""] * 48
food_with_multiplicity = [""] * 48
i = 0
for n in range(6):
for f in range(8):
nutr_with_multiplicity[i] = df1.get_row_by_index(n)[0]
food_with_multiplicity[i] = foods[f]
i += 1
df3.set_column("NUTR", nutr_with_multiplicity)
df3.set_column("FOOD", food_with_multiplicity)
# Populate with all these values
values = [
60,
8,
8,
40,
15,
70,
25,
60,
10,
20,
15,
35,
15,
15,
25,
15,
15,
20,
10,
10,
15,
15,
15,
10,
20,
0,
10,
40,
35,
30,
50,
20,
295,
770,
440,
430,
315,
400,
370,
450,
968,
2180,
945,
278,
1182,
896,
1329,
1397,
]
df3.add_column("amt", values)
# Create an AMPL instance
ampl = AMPL()
if argc > 1:
ampl.set_option("solver", argv[1])
# Read the model file
model_directory = argv[2] if argc == 3 else os.path.join("..", "models")
ampl.read(os.path.join(model_directory, "diet/diet.mod"))
# Assign data to NUTR, n_min and n_max
ampl.set_data(df1, "NUTR")
# Assign data to FOOD, f_min, f_max and cost
ampl.set_data(df2, "FOOD")
# Assign data to amt
ampl.set_data(df3)
# Solve the model
ampl.solve()
# Print out the result
print(
"Objective function value: {}".format(ampl.get_objective("Total_Cost").value())
)
# Get the values of the variable Buy in a dataframe
results = ampl.get_variable("Buy").get_values()
# Print
print(results)
