def test_overhead(self):
print "Testing overhead..."
#import cProfile
#import StringIO
#import pstats
for num_timesteps in self.num_timesteps:
for num_nodes in self.num_nodes:
#pr = cProfile.Profile(timer=self.timer)
#pr.enable()
print "%s timesteps * %s nodes" % (num_timesteps, num_nodes)
#Create a simulator object which will be run.
s = Simulator()
#Set the timesteps of the simulator.
timesteps = range(num_timesteps)
s.set_timesteps(timesteps)
#Create the network with initial data
n = Network(name="example network %s nodes %s timesteps" %
(num_nodes, num_timesteps))
for node_num in range(num_nodes):
n.add_node(
DummyNode(x=node_num,
y=node_num,
name="Node number %s" % node_num))
#Set the simulator's network to this network.
s.network = n
#Create an instance of the deficit allocation engine.
empty_engine = EmptyEngine(n)
#add the engine to the simulator
s.add_engine(empty_engine)
t = time.time()
#start the simulator
s.start()
self.result_matrix[num_timesteps][num_nodes] = (time.time() -
t)
#pr.disable()
#s = StringIO.StringIO()
#sortby = 'cumulative'
#ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
#ps.print_stats()
#with open('profile_%s_%s.txt' % (num_timesteps, num_nodes), 'w') as profile_result:
# profile_result.write(s.getvalue())
overhead_result = open('overhead_result.json', 'w')
overhead_result.write(json.dumps(self.result_matrix))
def test_engine_error(self):
""" Test an engine can raises a non-StopIteration error.
"""
s = Simulator(max_iterations=5)
s.network = Network("Iteration test network")
s.add_engine(TypeErrorEngine(None, error_on_iteration=1))
s.add_engine(CountEngine(None))
s.timesteps = [
0,
]
with self.assertRaises(TypeError):
s.start()
def test_engine_error(self):
""" Test an engine can raises a non-StopIteration error.
"""
s = Simulator(max_iterations=5)
s.network = Network("Iteration test network")
s.add_engine(TypeErrorEngine(None, error_on_iteration=1))
s.add_engine(CountEngine(None))
s.timesteps = [0, ]
with self.assertRaises(TypeError):
s.start()
def test_dict_overwrite(self):
"""
Test to ensure that dictionaries and other objects contained in history
are not overwritten by maintaining references to the same objects.
"""
print("Testing...")
network = Network("History Test Network")
network.add_node(HistoryTestNode(x=0, y=0, name="Test Node"))
s = Simulator()
s.network = network
s.timesteps = ['a', 'b', 'c']
s.start()
assert s.network.nodes[0]._history['property_dict'] == [{'test': 'a'}, {'test': 'b'}, {'test': 'c'}]
def test_overhead(self):
print("Testing overhead...")
#import cProfile
#import StringIO
#import pstats
for num_timesteps in self.num_timesteps:
for num_nodes in self.num_nodes:
#pr = cProfile.Profile(timer=self.timer)
#pr.enable()
print("%s timesteps * %s nodes"%(num_timesteps, num_nodes))
#Create a simulator object which will be run.
s = Simulator()
#Set the timesteps of the simulator.
timesteps = range(num_timesteps)
s.set_timesteps(timesteps)
#Create the network with initial data
n = Network(name="example network %s nodes %s timesteps"%(num_nodes, num_timesteps))
for node_num in range(num_nodes):
n.add_node(DummyNode(x=node_num, y=node_num, name="Node number %s"%node_num))
#Set the simulator's network to this network.
s.network = n
#Create an instance of the deficit allocation engine.
empty_engine = EmptyEngine(n)
#add the engine to the simulator
s.add_engine(empty_engine)
t = time.time()
#start the simulator
s.start()
self.result_matrix[num_timesteps][num_nodes] = (time.time()-t)
#pr.disable()
#s = StringIO.StringIO()
#sortby = 'cumulative'
#ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
#ps.print_stats()
#with open('profile_%s_%s.txt' % (num_timesteps, num_nodes), 'w') as profile_result:
# profile_result.write(s.getvalue())
overhead_result = open('overhead_result.json', 'w')
overhead_result.write(json.dumps(self.result_matrix))
def test_engine_stopping_iteration(self):
""" Test an engine terminating iteration
The engine must raise a `StopIteration` exception.
"""
s = Simulator(max_iterations=5)
s.network = Network("Iteration test network")
stopper_engine = StopperEngine(None, stop_on_iteration=2)
s.add_engine(stopper_engine)
engine = CountEngine(None)
s.add_engine(engine)
s.timesteps = [
0,
]
s.start()
assert engine.iteration == 1
assert engine.run_count == 1
assert stopper_engine.iteration == 2
def test_engine_iteration(self):
""" Test setting the global number of iterations in a simulator. """
s = Simulator(max_iterations=5)
s.network = Network("Iteration test network")
engine = CountEngine(None)
s.add_engine(engine)
s.timesteps = [
0,
]
s.start()
assert engine.iteration == 5
assert engine.run_count == 5
def test_default_iteration(self):
""" Test that the default simulation `max_iterations` is one. """
s = Simulator()
s.network = Network("Iteration test network")
engine = CountEngine(None)
s.add_engine(engine)
s.timesteps = [
0,
]
s.start()
assert engine.iteration == 1
assert engine.run_count == 1
def test_default_iteration(self):
""" Test that the default simulation `max_iterations` is one. """
s = Simulator()
s.network = Network("Iteration test network")
engine = CountEngine(None)
s.add_engine(engine)
s.timesteps = [0, ]
s.start()
assert engine.iteration == 1
assert engine.run_count == 1
def test_engine_iteration(self):
""" Test setting the global number of iterations in a simulator. """
s = Simulator(max_iterations=5)
s.network = Network("Iteration test network")
engine = CountEngine(None)
s.add_engine(engine)
s.timesteps = [0, ]
s.start()
assert engine.iteration == 5
assert engine.run_count == 5
def test_engine_stopping_iteration(self):
""" Test an engine terminating iteration
The engine must raise a `StopIteration` exception.
"""
s = Simulator(max_iterations=5)
s.network = Network("Iteration test network")
stopper_engine = StopperEngine(None, stop_on_iteration=2)
s.add_engine(stopper_engine)
engine = CountEngine(None)
s.add_engine(engine)
s.timesteps = [0, ]
s.start()
assert engine.iteration == 1
assert engine.run_count == 1
assert stopper_engine.iteration == 2
def test_dict_overwrite(self):
"""
Test to ensure that dictionaries and other objects contained in history
are not overwritten by maintaining references to the same objects.
"""
print("Testing...")
network = Network("History Test Network")
network.add_node(HistoryTestNode(x=0, y=0, name="Test Node"))
s = Simulator()
s.network = network
s.timesteps = ['a', 'b', 'c']
s.start()
assert s.network.nodes[0]._history['property_dict'] == [{
'test': 'a'
}, {
'test': 'b'
}, {
'test': 'c'
}]
'oct': 100.0,
'nov': 100.0,
'dec': 100.0,
}
# Network
network = ReservoirSystem(name="Allocation in a river system")
network.add_nodes(J1,J2,J3,J4,J5,J6,J7,U1,F1,F2,F3,F4,E1)
network.add_links(J1_J2,J2_J3,J2_J4,J3_U1,J3_F4,J4_J5,J5_J6,J6_J7,J7_E1,J5_F1,J6_F2,J7_F3
)
network.timestep = 86400 * 30 # one month
# Simulation
simulation = Simulator()
simulation.set_timesteps(timesteps)
simulation.network = network
engine = SimpleRouting(network)
simulation.add_engine(engine)
simulation.start()
for node in simulation.network.nodes:
print("%s: %s"%(node.name, node._history['inflow'][0]))
simulation.network.plot('inflow')
link2._max_flow = {0: 90.0, 1: 100.0, 2: 110.0, 3: 100.0, 4: 90.0, 5: 100.0}
link3._max_flow = {0: 310.0, 1: 300.0, 2: 300.0, 3: 300.0, 4: 310.0, 5: 300.0}
link4._max_flow = {0: 220.0, 1: 250.0, 2: 200.0, 3: 230.0, 4: 240.0, 5: 250.0}
link5._max_flow = {0: 30.0, 1: 40.0, 2: 20.0, 3: 30.0, 4: 30.0, 5: 10.0}
# Define network
network = WaterResourcesSystem(name='Priority based')
network.add_nodes(SR, IRR, URB, J1, In, Out)
network.add_links(link1, link2, link3, link4, link5)
# Run simulation
simulation = Simulator()
simulation.set_timesteps(timesteps)
simulation.network = network
engine = PriorityBased(network)
simulation.add_engine(engine)
simulation.start()
import seaborn
import matplotlib.pyplot as plt
plt.figure(1)
# but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with the Pyomo Plugin Demo Suite. If not, see <http://www.gnu.org/licenses/>. #Author: Majed Khadem, Silvia Padula, Khaled Mohamed, Stephen Knox, Julien Harou from components import UrbanNode, AgriculturalNode, SurfaceReservoir, Junction, RiverSection,CostMinimisationNetwork from engines import PyomoAllocation from pynsim import Simulator #create the simulator to run s = Simulator() timesteps = ["1", "2", "3", "4", "5", "6"] #add the timesteps to the simulator. The simulator may use these timesteps #to access timeseries data in the network. s.set_timesteps(timesteps) n = CostMinimisationNetwork(name="Cost Minimisation network") #Create the surface reservoir with a name, x and y coordinate. sr1 = SurfaceReservoir(x=1, y=2, name="SR1") #Add internal data to the reservoir. Internal data is signifiied by having an #underscore before the start of the variable name. #The surface reservoir type has an initial storage, inflow, min storage and max storage #so these are specified here.
n_YRB = Network('Yarmouk_reservoirs_network')
for us_n in nodes:
n_YRB.add_node(us_n)
for idx in reservoirs.idx:
if idx in W.keys():
n_YRB.add_link(W[idx])
if idx in C.keys():
n_YRB.add_link(C[idx])
if idx in U.keys():
n_YRB.add_link(U[idx])
n_YRB.add_institutions(Jordan_gvt, Israel_gvt, Syria_gvt)
# Simulator object that will be run
s = Simulator(network=n_YRB)
# Timesteps of the simulator
t = range(0, T) # months
s.set_timesteps(t)
# Engines
reservoirs_management = SimulationOfSyria(target=Syria_gvt)
treaty = TreatyOfPeace(target=Jordan_gvt)
pumping = ExchangeWithTiberias(target=Israel_gvt)
s.add_engine(reservoirs_management)
# if test alpha & beta possibilities
s.add_engine(treaty)
s.add_engine(pumping)
# Simulation
import random
import time
from pynsim import Simulator, Network, Node, Link, Institution, Engine
#Create a simulator object which will be run.
s = Simulator()
#Set the timesteps of the simulator.
timesteps = range(100)
s.set_timesteps(timesteps)
#Create the network with initial data
n = Network(name="example network")
num_nodes = 10000
for node_num in range(num_nodes):
n.add_node(Node(x=node_num, y=node_num, name="Node number %s"%node_num))
for link_num in range(num_nodes):
start_node = random.choice(n.nodes)
#Make sure the start and end node aren't the same
while True:
end_node = random.choice(n.nodes)
if end_node.name != start_node.name:
break
n.add_link(Link(name="Link number %s"%link_num, start_node=start_node, end_node=end_node))
num_institutions = 100
for inst_num in range(num_institutions):
i = Institution(name="Instution Number %s"%inst_num)
i.add_nodes(*n.nodes)
from components import CitrusFarm, VegetableFarm, SurfaceReservoir, RiverSection
from components import WaterDepartment, IrrigationDecisionMaker
from engines import DeficitAllocation
from pynsim import Simulator, Network
#Create a simulator object which will be run.
s = Simulator()
#Set the timesteps of the simulator.
timesteps = [
"2014-01-01", "2014-02-01", "2014-03-01", "2014-04-01", "2014-05-01"
]
s.set_timesteps(timesteps)
#Create the network with initial data
n = Network(name="example network")
#set some initial data on this network.
n.incoming_water_qty = {
"2014-01-01": 50,
"2014-02-01": 61,
"2014-03-01": 65,
"2014-04-01": 45,
"2014-05-01": 30
}
#Add nodes to the network
#All nodes have an x, y and name. The surface reservoir also has 'release' which
#can be set on creation or after.
def run_simulation(diversion_pos, discharge_data, draw=False, queue=None):
"""Run the simulation based on a given position of diversion nodes.
A fixed river network structure is assumed here.
N1
|
N2 N4
| |
N3 N5
\ /
N6
|
N7 N9
| |
N8 N10
| /
N11
|
N12
"""
N1 = RiverNode(x=0, y=7, name='N1')
N1.dQdx = copy.deepcopy(discharge_data['N1'])
N2 = RiverNode(x=0, y=6, name='N2')
N2.dQdx = copy.deepcopy(discharge_data['N2'])
N3 = RiverNode(x=0, y=5, name='N3')
N3.dQdx = copy.deepcopy(discharge_data['N3'])
N4 = RiverNode(x=2, y=6, name='N4')
N4.dQdx = copy.deepcopy(discharge_data['N4'])
N5 = RiverNode(x=2, y=5, name='N5')
N5.dQdx = copy.deepcopy(discharge_data['N5'])
N6 = RiverNode(x=1, y=4, name='N6')
N6.dQdx = copy.deepcopy(discharge_data['N6'])
N7 = RiverNode(x=1, y=3, name='N7')
N7.dQdx = copy.deepcopy(discharge_data['N7'])
N8 = RiverNode(x=1, y=2, name='N8')
N8.dQdx = copy.deepcopy(discharge_data['N8'])
N9 = RiverNode(x=3, y=3, name='N9')
N9.dQdx = copy.deepcopy(discharge_data['N9'])
N10 = RiverNode(x=3, y=2, name='N10')
N10.dQdx = copy.deepcopy(discharge_data['N10'])
N11 = RiverNode(x=2, y=1, name='N11')
N11.dQdx = copy.deepcopy(discharge_data['N11'])
N12 = RiverNode(x=2, y=0, name='N12')
N12.dQdx = copy.deepcopy(discharge_data['N12'])
node_dict = {
0: N1,
1: N2,
2: N3,
3: N4,
4: N5,
5: N6,
6: N7,
7: N8,
8: N9,
9: N10,
10: N11,
11: N12
}
# Add a diversion node based on the position parameter
div_node = Diversion(x=node_dict[diversion_pos].x,
y=node_dict[diversion_pos].y,
name='Diversion')
div_node.dQdx = node_dict[diversion_pos].dQdx
div_node.demand = 0.5
node_dict[diversion_pos] = div_node
L1 = GenericLink(start_node=node_dict[0], end_node=node_dict[1], name='L1')
L2 = GenericLink(start_node=node_dict[1], end_node=node_dict[2], name='L2')
L3 = GenericLink(start_node=node_dict[2], end_node=node_dict[5], name='L3')
L4 = GenericLink(start_node=node_dict[3], end_node=node_dict[4], name='L4')
L5 = GenericLink(start_node=node_dict[4], end_node=node_dict[5], name='L5')
L6 = GenericLink(start_node=node_dict[5], end_node=node_dict[6], name='L6')
L7 = GenericLink(start_node=node_dict[6], end_node=node_dict[7], name='L7')
L8 = GenericLink(start_node=node_dict[7],
end_node=node_dict[10],
name='L8')
L9 = GenericLink(start_node=node_dict[8], end_node=node_dict[9], name='L9')
L10 = GenericLink(start_node=node_dict[9],
end_node=node_dict[10],
name='L10')
L11 = GenericLink(start_node=node_dict[10],
end_node=node_dict[11],
name='L11')
river_network = RiverNetwork(name='River network with diversion')
river_network.add_nodes(*node_dict.values())
river_network.add_links(L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11)
if draw:
river_network.draw(block=False)
routing = Routing(river_network)
simulation = Simulator()
simulation.network = river_network
simulation.add_engine(routing)
simulation.set_timesteps([1])
simulation.start()
if queue is None:
return simulation.network.discharge
else:
queue.put(simulation.network.discharge)