def array_to_ampl_dataframe(
n_banks, liability): #change the name of liability to 2d array? TODO
ampl_liab = DataFrame(
('Banks', 'Banks2'),
'liability') #TODO : set 'liability' to parameter name, can get it?
ampl_liab.setValues({(i, j): liability[i][j]
for i in xrange(n_banks) for j in xrange(n_banks)})
return ampl_liab
def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
ampl = AMPL()
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model file
modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
ampl.read(os.path.join(modelDirectory, 'diet/diet.mod'))
foods = ['BEEF', 'CHK', 'FISH', 'HAM', 'MCH', 'MTL', 'SPG', 'TUR']
costs = [3.59, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
fmin = [2, 2, 2, 2, 2, 2, 2, 2]
fmax = [10, 10, 10, 10, 10, 10, 10, 10]
df = DataFrame('FOOD')
df.setColumn('FOOD', foods)
df.addColumn('cost', costs)
df.addColumn('f_min', fmin)
df.addColumn('f_max', fmax)
ampl.setData(df, 'FOOD')
nutrients = ['A', 'C', 'B1', 'B2', 'NA', 'CAL']
nmin = [700, 700, 700, 700, 0, 16000]
nmax = [20000, 20000, 20000, 20000, 50000, 24000]
df = DataFrame('NUTR')
df.setColumn('NUTR', nutrients)
df.addColumn('n_min', nmin)
df.addColumn('n_max', nmax)
ampl.setData(df, 'NUTR')
amounts = [[60, 8, 8, 40, 15, 70, 25, 60],
[20, 0, 10, 40, 35, 30, 50, 20],
[10, 20, 15, 35, 15, 15, 25, 15],
[15, 20, 10, 10, 15, 15, 15, 10],
[928, 2180, 945, 278, 1182, 896, 1329, 1397],
[295, 770, 440, 430, 315, 400, 379, 450]]
df = DataFrame(('NUTR', 'FOOD'), 'amt')
df.setValues({(nutrient, food): amounts[i][j]
for i, nutrient in enumerate(nutrients)
for j, food in enumerate(foods)})
ampl.setData(df)
ampl.solve()
print('Objective: {}'.format(ampl.getObjective('total_cost').value()))
except Exception as e:
print(e)
raise
def testDict(self):
dic = {"aa": "bb", "c": "a"}
self.assertEqual(dic, DataFrame.fromDict(dic).toDict())
dic = {1: 2}
self.assertEqual(dic, DataFrame.fromDict(dic).toDict())
dic = {1: 2, 3: 4}
self.assertEqual(dic, DataFrame.fromDict(dic).toDict())
dic = {2.0: ("a", "b"), 3: ("1", "2")}
self.assertEqual(dic, DataFrame.fromDict(dic).toDict())
dic = {(2.0, "c"): ("a", "b"), (3, "a"): ("1", "2")}
self.assertEqual(dic, DataFrame.fromDict(dic).toDict())
df = DataFrame("x", "y")
dic = {1: 12, 2: 23}
df.setValues(dic)
self.assertEqual(dic, df.toDict())
df = DataFrame("x", ["y", "z"])
dic = {1: (12, 2), 2: (23, -1)}
df.setValues(dic)
self.assertEqual(dic, df.toDict())
df = DataFrame("x", ["y", "z"])
df.setValues({1: [1, 2]})
self.assertEqual({1: (1, 2)}, df.toDict())
def testIter(self):
df = DataFrame("x", "y")
dic = {1: 12, 2: 23}
df.setValues(dic)
for row in df:
self.assertTrue(tuple(row) in [(1, 12), (2, 23)])
'asked_mem': 0,
'asked_disk': 0,
'frees_mem': leaves_mem[i],
'frees_cpu': leaves_cpu[i],
'frees_disk': leaves_disk[i],
'frees_arrival': leaves_arrival[i]
}
# Set the ordered timestamps
timestamps = times + leaves_time
timestamps.sort()
ampl.set['timestamps'] = timestamps
# Set profits
df = DataFrame(('timestamps'), 'profit_federate')
df.setValues({t: events[t]['profit_federate'] for t in events.keys()})
ampl.setData(df)
df = DataFrame(('timestamps'), 'profit_local')
df.setValues({t: events[t]['profit_local'] for t in events.keys()})
ampl.setData(df)
df = DataFrame(('timestamps'), 'profit_reject')
df.setValues({t: events[t]['profit_reject'] for t in events.keys()})
ampl.setData(df)
# Set asked resources
df = DataFrame(('timestamps'), 'asked_cpu')
df.setValues({t: events[t]['asked_cpu'] for t in events.keys()})
ampl.setData(df)
df = DataFrame(('timestamps'), 'asked_mem')
df.setValues({t: events[t]['asked_mem'] for t in events.keys()})
ampl.setData(df)
def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
ampl = AMPL()
ampl.setOption('solver', 'cplexamp')
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model file
modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
ampl.read(os.path.join(modelDirectory, 'Dr/pigskin_updated1.mod'))
period0 = [0]
df = DataFrame('ONLY0')
df.setColumn('ONLY0', period0)
ampl.setData(df, 'ONLY0')
period0toends = [0, 1]
df = DataFrame('PERIOD0_TO_END')
df.setColumn('PERIOD0_TO_END', period0toends)
ampl.setData(df, 'PERIOD0_TO_END')
period1toends = [1]
df = DataFrame('PERIOD1_TO_END')
df.setColumn('PERIOD1_TO_END', period1toends)
ampl.setData(df, 'PERIOD1_TO_END')
products = ['1P', '2P']
df = DataFrame('PRODUCT')
df.setColumn('PRODUCT', products)
ampl.setData(df, 'PRODUCT')
resources = ['1R', '2R']
df = DataFrame('RESOURCE')
df.setColumn('RESOURCE', resources)
ampl.setData(df, 'RESOURCE')
scenarios = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
]
df = DataFrame('SCENARIO')
df.setColumn('SCENARIO', scenarios)
ampl.setData(df, 'SCENARIO')
inv0prod = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0]]
df = DataFrame(('SCENARIO', 'PRODUCT'), 'Inv0prod')
df.setValues({(scenario, product): inv0prod[s][i]
for s, scenario in enumerate(scenarios)
for i, product in enumerate(products)})
ampl.setData(df)
prodcost = [[12.5], [12.55]]
df = DataFrame(('PRODUCT', 'PERIOD1_TO_END'), 'Prodcost')
df.setValues({(product, period1toend): prodcost[i][t]
for i, product in enumerate(products)
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
Resource = [[16, 100], [24, 200]]
df = DataFrame(('PRODUCT', 'RESOURCE'), 'Resource')
df.setValues({(product, resource): Resource[i][r]
for i, product in enumerate(products)
for r, resource in enumerate(resources)})
ampl.setData(df)
avail = [[138516], [278847]]
df = DataFrame(('RESOURCE', 'PERIOD1_TO_END'), 'avail')
df.setValues({(resource, period1toend): avail[r][t]
for r, resource in enumerate(resources)
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
perhold = [[0.88], [0.88]]
df = DataFrame(('PRODUCT', 'PERIOD1_TO_END'), 'perhold')
df.setValues({(product, period1toend): perhold[i][t]
for i, product in enumerate(products)
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
prob = [
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333
]
df = DataFrame(('SCENARIO'), 'prob')
df.setValues({(scenario): prob[s]
for s, scenario in enumerate(scenarios)})
ampl.setData(df)
MPC = [800]
df = DataFrame(('PERIOD1_TO_END'), 'MPC')
df.setValues({(period1toend): MPC[t]
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
MS = [100]
df = DataFrame(('PERIOD1_TO_END'), 'MS')
df.setValues({(period1toend): MS[t]
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
Purchase_cost = [[10], [10]]
df = DataFrame(('PRODUCT', 'PERIOD1_TO_END'), 'Purchase_cost')
df.setValues({(product, period1toend): Purchase_cost[i][t]
for i, product in enumerate(products)
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
Demand = [[[216], [191]], [[290], [330]], [[224], [194]], [[283],
[329]],
[[159], [164]], [[215], [349]], [[247], [201]], [[201],
[328]],
[[343], [230]], [[326], [278]], [[319], [342]], [[223],
[298]],
[[292], [191]], [[262], [284]], [[285], [200]], [[296],
[346]],
[[210], [318]], [[200], [257]], [[314], [336]], [[205],
[290]],
[[174], [184]], [[258], [290]], [[284], [158]], [[264],
[312]],
[[228], [333]], [[239], [255]], [[190], [322]], [[344],
[219]],
[[280], [241]], [[186], [254]]]
df = DataFrame(('SCENARIO', 'PERIOD1_TO_END', 'PRODUCT'), 'Demand')
df.setValues({(scenario, period1toend, product): Demand[s][t][i - 1]
for s, scenario in enumerate(scenarios)
for t, period1toend in enumerate(period1toends)
for i, product in enumerate(products)})
ampl.setData(df)
ampl.solve()
print('Objective: {}'.format(ampl.getObjective('Total_cost').value()))
produce = ampl.getVariable('Produce')
df = produce.getValues()
# Print them
print(df)
# Get the values of the variable in a dataframe object
inventory = ampl.getVariable('Inventory')
df = inventory.getValues()
# Print them
print(df)
except Exception as e:
print(e)
raise
nnodes = nnodes / 10
i += 1
formatString = f"{{:0{i}d}}"
nodes = {
formatString.format(value): p.node_coords[index + 1]
for index, value in enumerate(p.get_nodes())
}
return nodes
# Set problem data from tsp file
nodes = getDictFromTspFile(tspFile)
# Pass them to AMPL using a dataframe
df = DataFrame(index=[('NODES')], columns=['hpos', 'vpos'])
df.setValues(nodes)
ampl.setData(df, "NODES")
# Set some globals that never change during the execution of the problem
NODES = set(nodes.keys())
CPOINTS = {
node: complex(coordinate[0], coordinate[1])
for (node, coordinate) in nodes.items()
}
# Plot helpers
def plotTours(tours: list, points_coordinate: dict):
# Plot all the tours in the list each with a different color
colors = ['b', 'g', 'c', 'm', 'y', 'k']
for i, tour in enumerate(tours):
