def setUp(self):
# self.net = Network(inputfile="tests/Case1.inp")
self.net = Network(inputfile="Case1.inp")
self.pp = PhreeqPython()
self.vic = Victoria(self.net, self.pp)
self.solutions = {}
sol0 = self.pp.add_solution({'Ca': 0, 'units': 'mmol/l'})
sol1 = self.pp.add_solution({'Ca': 1, 'units': 'mmol/l'})
self.solutions[0] = sol0
self.solutions[self.net.reservoirs['1'].uid] = sol1
def __init__(self, filename='anytown_master.inp'):
# load network
self.network = Network(filename)
self.network.solve()
# for control
self.speed_limit = (0.7, 1.2)
# danger zone
self.junction_threshold = (15, 120)
self.state = None
# function to get the effeciency of the pump(s)
self.eff = None
# speed increase/decrease resolution
self.stepsize = 0.1
self.network.solve()
self._calc_effeciency('E1')
def main():
network = Network(inp_file)
num_junctions = 0
num_nodes = network.ep.ENgetcount(0)
for node in range(num_nodes):
if network.ep.ENgetnodetype(node + 1) == 0:
num_junctions += 1
print("Number of junctions: ", num_junctions, "\n")
avg_initial_pressure_at_each_junc = average_initial_pressures(
solve_and_return_pressures(num_junctions))
scores = add_tank_get_score(num_junctions,
avg_initial_pressure_at_each_junc)
best_junction = network.ep.ENgetnodeid(int(np.argmax(scores) + 1))
junction_ids = []
for junction in range(num_junctions):
junction_ids.append(network.ep.ENgetnodeid(junction + 1))
print("\nIndex of best junction: ", np.argmax(scores) + 1)
print("ID of best junction: ", best_junction,
"\nMin avg network pressure with optimized tank: ", np.max(scores))
original_score = score_pressure_array(
solve_and_return_pressures(num_junctions))
print("\nMin avg network pressure without buffer tank: ", original_score)
score_dataframe = pd.DataFrame({
"Junction ID": junction_ids,
"Min Avg Network Pressure": scores,
"Tank Elevation": tank_elev_array
})
score_dataframe.index = score_dataframe.index + 1
score_dataframe.index.name = "Junction Index"
sorted_scores = score_dataframe.sort_values("Min Avg Network Pressure",
ascending=False)
print("\nTank locations sorted by minimum average network pressure:\n")
print(sorted_scores)
print("\nSaving CSV...")
sorted_scores.to_csv("Optimal_Location_List.csv")
class TestNetwork(object):
@classmethod
def setup_class(self):
self.network = Network()
self.network.solve()
@classmethod
def teardown(self):
self.network.ep.ENclose()
def test01_multiple_pumps(self):
network = self.network
network.add_reservoir("R1", 0, 0, 0)
network.add_junction("J1", 1, 0)
network.add_junction("J2", 2, 0)
network.add_junction("J3", 3, 0)
network.add_junction("J4", 4, 0)
network.add_reservoir("R2", 5, 0, 0)
network.add_pipe("P1", "R1", "J1", 200, 100)
network.add_pipe("P2", "J2", "J4", 200, 100)
network.add_pipe("P3", "J3", "J4", 200, 100)
network.add_pipe("P4", "J4", "R2", 200, 100)
P1 = network.add_pump("PU1", "J1", "J2", 1)
P2 = network.add_pump("PU2", "J1", "J3", 1)
C1 = network.add_curve("1", [[100, 30], [200, 20], [300, 10]])
C2 = network.add_curve("2", [[100, 40], [200, 25], [300, 10]])
P1.curve = C1
P2.curve = C2
network.solve()
assert_almost_equal(P1.flow, 225.88, 2)
assert_almost_equal(P2.flow, 248.14, 2)
def solve_and_return_pressures(num_junctions):
# function to calculate the pressure of every node at any timestep
# call EPANET to load the network
network = Network(inp_file)
network.ep.ENopen(inp_file)
network.ep.ENopenH()
network.ep.ENinitH()
# temp array for storing pressure of each junction at each timestep
temp_pressure_array = np.array([])
# big array of all pressures for all junctions at all timesteps
perm_pressure_array = np.empty((num_junctions, 0))
# solve for all timesteps and store each node's pressure at each timestep in an array
runtime = 0
while network.ep.ENnextH() > 0:
network.ep.ENrunH()
for junction in range(num_junctions):
junction_pressure = network.ep.ENgetnodevalue(junction + 1, 11)
temp_pressure_array = np.append(temp_pressure_array,
junction_pressure)
perm_pressure_array = np.append(perm_pressure_array,
temp_pressure_array.reshape(
num_junctions, 1),
axis=1)
runtime += network.ep.ENgettimeparam(1)
temp_pressure_array = []
network.ep.ENcloseH()
network.ep.ENdeleteproject()
return perm_pressure_array
class NetworkAgent:
def __init__(self, filename='anytown_master.inp'):
# load network
self.network = Network(filename)
self.network.solve()
# for control
self.speed_limit = (0.7, 1.2)
# danger zone
self.junction_threshold = (15, 120)
self.state = None
# function to get the effeciency of the pump(s)
self.eff = None
# speed increase/decrease resolution
self.stepsize = 0.1
self.network.solve()
self._calc_effeciency('E1')
def _calc_effeciency(self, curve_id):
curve = None
for x in self.network.curves:
if curve_id in x.uid:
flow = [ value[0] for value in x.values]
y = [value[1] for value in x.values]
curve = np.poly1d(np.polyfit(flow,y,4))
if not curve:
logging.info('no curve found with id{}'.format(curve_id))
self.eff = curve
def reset(self):
# epynet has its own reset function
# self._calc_effeciency('E1')
#optimize
from scipy.optimize import minimize
# initial guess is 0.8 thats the speed
self.res = minimize(self.find_opt,1.2, method='Nelder-Mead', options={'disp':True}, bounds = self.speed_limit)
print(self.res.x)
self.network.reset()
def find_opt(self, x):
"""
x : pump's speed
returns: effeciency
"""
# change speed
self.network.pumps['78'].speed = x
self.network.solve()
eff = self.calc_eff()
# - sign because we need the maximum value
return - eff
def step(self):
x = self.network.solve()
print('solve function returns: {}'.format(x))
def calc_eff(self):
# get only one pump with id 78
# complicated networks will be harder than this
flow = self.network.pumps['78'].flow
print('flow is: {}'.format(flow))
speed = self.network.pumps['78'].speed
true_flow = float(flow/speed)
return self.eff(true_flow)
def junction_head(self):
return list(self.network.junctions.head)
def junction_demand(self):
return list(self.network.junctions.demand)
def junction_pressure(self):
return list(self.network.junctions.pressure)
def _action_increase_speed(self):
# check if it's within the limits
if self.network.pumps['78'].speed + self.stepsize > self.speed_limit[1]:
return
self.network.pumps['78'].speed += self.stepsize
def _action_decrease_speed(self):
if self.network.pumps['78'].speed - self.stepsize < self.speed_limit[1]:
return
self.network.pumps['78'].speed -= self.stepsize
def setup_class(self):
self.network = Network('tests/testnetwork.inp')
self.network.solve()
class wds():
"""Gym-like environment for water distribution systems."""
def __init__(self,
wds_name='anytown_master',
speed_increment=.05,
episode_len=10,
pump_groups=[['78', '79']],
total_demand_lo=.3,
total_demand_hi=1.1,
reset_orig_pump_speeds=False,
reset_orig_demands=False,
seed=None):
self.seedNum = seed
if self.seedNum:
np.random.seed(self.seedNum)
else:
np.random.seed()
pathToRoot = os.path.dirname(os.path.realpath(__file__))
pathToWDS = os.path.join(pathToRoot, 'water_networks',
wds_name + '.inp')
self.wds = Network(pathToWDS)
self.demandDict = self.build_demand_dict()
self.pumpGroups = pump_groups
self.pump_speeds = np.ones(shape=(len(self.pumpGroups)),
dtype=np.float32)
self.pumpEffs = np.empty(shape=(len(self.pumpGroups)),
dtype=np.float32)
nomHCurvePtsDict, nomECurvePtsDict = self.get_performance_curve_points(
)
nomHCurvePoliDict = self.fit_polinomials(nomHCurvePtsDict,
degree=2,
encapsulated=True)
self.nomECurvePoliDict = self.fit_polinomials(nomECurvePtsDict,
degree=4,
encapsulated=True)
self.sumOfDemands = sum(
[demand for demand in self.wds.junctions.basedemand])
self.demandRandomizer = self.build_truncnorm_randomizer(lo=.7,
hi=1.3,
mu=1.0,
sigma=1.0)
# Theoretical bounds of {head, efficiency}
peak_heads = []
for key in nomHCurvePoliDict.keys():
max_q = np.max(nomHCurvePtsDict[key][:, 0])
opti_result = minimize(-nomHCurvePoliDict[key],
x0=1,
bounds=[(0, max_q)])
peak_heads.append(nomHCurvePoliDict[key](opti_result.x[0]))
peak_effs = []
for key in nomHCurvePoliDict.keys():
max_q = np.max(nomHCurvePtsDict[key][:, 0])
q_list = np.linspace(0, max_q, 10)
head_poli = nomHCurvePoliDict[key]
eff_poli = self.nomECurvePoliDict[key]
opti_result = minimize(-eff_poli, x0=1, bounds=[(0, max_q)])
peak_effs.append(eff_poli(opti_result.x[0]))
self.peakTotEff = np.prod(peak_effs)
# Reward control
self.dimensions = len(self.pumpGroups)
self.episodeLength = episode_len
self.headLimitLo = 15
self.headLimitHi = 120
self.maxHead = np.max(peak_heads)
self.rewScale = [5, 8, 3] # eff, head, pump
self.baseReward = +1
self.bumpPenalty = -1
self.distanceRange = .5
self.wrongMovePenalty = -1
self.lazinessPenalty = -1
# ----- ----- ----- ----- -----
# Tweaking reward
# ----- ----- ----- ----- -----
#maxReward = 5
# ----- ----- ----- ----- -----
self.maxReward = +1
self.minReward = -1
# Inner variables
self.spec = None
self.metadata = None
self.totalDemandLo = total_demand_lo
self.totalDemandHi = total_demand_hi
self.speedIncrement = speed_increment
self.speedLimitLo = .7
self.speedLimitHi = 1.2
self.validSpeeds = np.arange(self.speedLimitLo,
self.speedLimitHi + .001,
self.speedIncrement,
dtype=np.float32)
self.resetOrigPumpSpeeds = reset_orig_pump_speeds
self.resetOrigDemands = reset_orig_demands
self.optimized_speeds = np.empty(shape=(len(self.pumpGroups)),
dtype=np.float32)
self.optimized_speeds.fill(np.nan)
self.optimized_value = np.nan
self.previous_distance = np.nan
# initialization of {observation, steps, done}
observation = self.reset(training=False)
self.action_space = gym.spaces.Discrete(2 * self.dimensions + 1)
self.observation_space = gym.spaces.Box(
low=-1,
high=+1,
shape=(len(self.wds.junctions) + len(self.pumpGroups), ),
dtype=np.float32)
# for one-shot tests
self.one_shot = rs.rs(target=self.reward_to_deap,
dims=self.dimensions,
limit_lo=self.speedLimitLo,
limit_hi=self.speedLimitHi,
step_size=self.speedIncrement,
maxfev=1)
def step(self, action, training=True):
""" Reward computed from the Euclidean distance between the speed of the pumps
and the optimized speeds."""
self.steps += 1
self.done = (self.steps == self.episodeLength)
group_id = action // 2
command = action % 2
if training:
if group_id != self.dimensions:
self.n_siesta = 0
first_pump_in_grp = self.wds.pumps[self.pumpGroups[group_id]
[0]]
if command == 0:
if first_pump_in_grp.speed < self.speedLimitHi:
for pump in self.pumpGroups[group_id]:
self.wds.pumps[pump].speed += self.speedIncrement
self.update_pump_speeds()
distance = np.linalg.norm(self.optimized_speeds -
self.pump_speeds)
if distance < self.previous_distance:
# ----- ----- ----- ----- -----
# Tweaking reward
# ----- ----- ----- ----- -----
#reward = distance * self.baseReward / self.distanceRange
reward = distance * self.baseReward / self.distanceRange / self.maxReward
# ----- ----- ----- ----- -----
else:
reward = self.wrongMovePenalty
self.previous_distance = distance
else:
self.n_bump += 1
reward = self.bumpPenalty
else:
if first_pump_in_grp.speed > self.speedLimitLo:
for pump in self.pumpGroups[group_id]:
self.wds.pumps[pump].speed -= self.speedIncrement
self.update_pump_speeds()
distance = np.linalg.norm(self.optimized_speeds -
self.pump_speeds)
if distance < self.previous_distance:
# ----- ----- ----- ----- -----
# Tweaking reward
# ----- ----- ----- ----- -----
#reward = distance * self.baseReward / self.distanceRange
reward = distance * self.baseReward / self.distanceRange / self.maxReward
# ----- ----- ----- ----- -----
else:
reward = self.wrongMovePenalty
self.previous_distance = distance
else:
self.n_bump += 1
reward = self.bumpPenalty
else:
self.n_siesta += 1
value = self.get_state_value()
if self.n_siesta == 3:
self.done = True
if value / self.optimized_value > .98:
# ----- ----- ----- ----- -----
# Tweaking reward
# ----- ----- ----- ----- -----
#reward = 5
reward = 5 / self.maxReward
# ----- ----- ----- ----- -----
else:
reward = self.lazinessPenalty
else:
if value / self.optimized_value > .98:
reward = self.n_siesta * self.baseReward
else:
reward = self.lazinessPenalty
self.wds.solve()
else:
if group_id != self.dimensions:
self.n_siesta = 0
first_pump_in_grp = self.wds.pumps[self.pumpGroups[group_id]
[0]]
if command == 0:
if first_pump_in_grp.speed < self.speedLimitHi:
for pump in self.pumpGroups[group_id]:
self.wds.pumps[pump].speed += self.speedIncrement
else:
self.n_bump += 1
else:
if first_pump_in_grp.speed > self.speedLimitLo:
for pump in self.pumpGroups[group_id]:
self.wds.pumps[pump].speed -= self.speedIncrement
else:
self.n_bump += 1
else:
self.n_siesta += 1
if self.n_siesta == 3:
self.done = True
self.wds.solve()
reward = self.get_state_value()
observation = self.get_observation()
return observation, reward, self.done, {}
def reset(self, training=True):
if training:
if self.resetOrigDemands:
self.restore_original_demands()
else:
self.randomize_demands()
self.optimize_state()
## One-shot begins
# self.optimize_state_with_one_shot()
# if self.optimized_value == 0:
# self.optimized_value = .01
## One-shot ends
if self.resetOrigPumpSpeeds:
initial_speed = 1.
for pump in self.wds.pumps:
pump.speed = initial_speed
else:
for pump_grp in self.pumpGroups:
initial_speed = np.random.choice(self.validSpeeds)
for pump in pump_grp:
self.wds.pumps[pump].speed = initial_speed
else:
if self.resetOrigPumpSpeeds:
initial_speed = 1.
for pump in self.wds.pumps:
pump.speed = initial_speed
else:
for pump_grp in self.pumpGroups:
initial_speed = np.random.choice(self.validSpeeds)
for pump in pump_grp:
self.wds.pumps[pump].speed = initial_speed
self.wds.solve()
observation = self.get_observation()
self.done = False
self.steps = 0
self.n_bump = 0
self.n_siesta = 0
return observation
def seed(self, seed=None):
"""Collecting seeds."""
return [seed]
def optimize_state(self):
speeds, target_val, _ = nm.minimize(self.reward_to_scipy,
self.dimensions)
self.optimized_speeds = speeds
self.optimized_value = -target_val
def optimize_state_with_one_shot(self):
speeds, target_val, _ = self.one_shot.maximize()
self.optimized_speeds = speeds
self.optimized_value = target_val
def fit_polinomials(self, pts_dict, degree, encapsulated=False):
"""Fitting polinomials to points stored in dict."""
polinomials = dict()
if encapsulated:
for curve in pts_dict:
polinomials[curve] = np.poly1d(
np.polyfit(pts_dict[curve][:, 0], pts_dict[curve][:, 1],
degree))
else:
for curve in pts_dict:
polinomials[curve] = np.polyfit(pts_dict[curve][:, 0],
pts_dict[curve][:, 1], degree)
return polinomials
def get_performance_curve_points(self):
"""Reader for H(Q) and P(Q) curves."""
head_curves = dict()
eff_curves = dict()
# Loading data to dictionary
for curve in self.wds.curves:
if curve.uid[0] == 'H': # this is an H(Q) curve
head_curves[curve.uid[1:]] = np.empty([len(curve.values), 2],
dtype=np.float32)
for i, op_pnt in enumerate(curve.values):
head_curves[curve.uid[1:]][i, 0] = op_pnt[0]
head_curves[curve.uid[1:]][i, 1] = op_pnt[1]
for curve in self.wds.curves:
if curve.uid[0] == 'E': # this is an E(Q) curve
eff_curves[curve.uid[1:]] = np.empty([len(curve.values), 2],
dtype=np.float32)
for i, op_pnt in enumerate(curve.values):
eff_curves[curve.uid[1:]][i, 0] = op_pnt[0]
eff_curves[curve.uid[1:]][i, 1] = op_pnt[1]
# Checking consistency
for head_key in head_curves.keys():
if all(head_key != eff_key for eff_key in eff_curves.keys()):
print('\nInconsistency in H(Q) and P(Q) curves.\n')
raise IndexError
return head_curves, eff_curves
def get_junction_heads(self):
junc_heads = np.empty(shape=(len(self.wds.junctions), ),
dtype=np.float32)
for junc_id, junction in enumerate(self.wds.junctions):
junc_heads[junc_id] = junction.head
return junc_heads
def get_observation(self):
heads = (2 * self.get_junction_heads() / self.maxHead) - 1
self.update_pump_speeds()
speeds = self.pump_speeds / self.speedLimitHi
return np.concatenate([heads, speeds])
def restore_original_demands(self):
for junction in self.wds.junctions:
junction.basedemand = self.demandDict[junction.uid]
def build_truncnorm_randomizer(self, lo, hi, mu, sigma):
randomizer = stats.truncnorm((lo - mu) / sigma, (hi - mu) / sigma,
loc=mu,
scale=sigma)
return randomizer
def randomize_demands(self):
target_sum_of_demands = self.sumOfDemands * (
self.totalDemandLo + np.random.rand() *
(self.totalDemandHi - self.totalDemandLo))
sum_of_random_demands = 0
if self.seedNum:
for junction in self.wds.junctions:
junction.basedemand = (
self.demandDict[junction.uid] * self.demandRandomizer.rvs(
random_state=self.seedNum *
int(np.abs(np.floor(junction.coordinates[0])))))
sum_of_random_demands += junction.basedemand
else:
for junction in self.wds.junctions:
junction.basedemand = (self.demandDict[junction.uid] *
self.demandRandomizer.rvs())
sum_of_random_demands += junction.basedemand
for junction in self.wds.junctions:
junction.basedemand *= target_sum_of_demands / sum_of_random_demands
def calculate_pump_efficiencies(self):
for i, group in enumerate(self.pumpGroups):
pump = self.wds.pumps[group[0]]
curve_id = pump.curve.uid[1:]
pump_head = pump.downstream_node.head - pump.upstream_node.head
eff_poli = self.nomECurvePoliDict[curve_id]
self.pumpEffs[i] = eff_poli(pump.flow / pump.speed)
def build_demand_dict(self):
demand_dict = dict()
for junction in self.wds.junctions:
demand_dict[junction.uid] = junction.basedemand
return demand_dict
def get_state_value_separated(self):
self.calculate_pump_efficiencies()
pump_ok = (self.pumpEffs < 1).all() and (self.pumpEffs > 0).all()
if pump_ok:
heads = np.array([head for head in self.wds.junctions.head])
invalid_heads_count = (np.count_nonzero(heads < self.headLimitLo) +
np.count_nonzero(heads > self.headLimitHi))
valid_heads_ratio = 1 - (invalid_heads_count / len(heads))
total_demand = sum(
[junction.basedemand for junction in self.wds.junctions])
total_tank_flow = sum(
[tank.inflow + tank.outflow for tank in self.wds.tanks])
demand_to_total = total_demand / (total_demand + total_tank_flow)
total_efficiency = np.prod(self.pumpEffs)
eff_ratio = total_efficiency / self.peakTotEff
else:
eff_ratio = 0
valid_heads_ratio = 0
demand_to_total = 0
return eff_ratio, valid_heads_ratio, demand_to_total
def get_state_value(self):
self.calculate_pump_efficiencies()
pump_ok = (self.pumpEffs < 1).all() and (self.pumpEffs > 0).all()
if pump_ok:
heads = np.array([head for head in self.wds.junctions.head])
invalid_heads_count = (np.count_nonzero(heads < self.headLimitLo) +
np.count_nonzero(heads > self.headLimitHi))
valid_heads_ratio = 1 - (invalid_heads_count / len(heads))
total_demand = sum(
[junction.basedemand for junction in self.wds.junctions])
total_tank_flow = sum(
[tank.inflow + tank.outflow for tank in self.wds.tanks])
demand_to_total = total_demand / (total_demand + total_tank_flow)
total_efficiency = np.prod(self.pumpEffs)
reward = (self.rewScale[0] * total_efficiency / self.peakTotEff +
self.rewScale[1] * valid_heads_ratio +
self.rewScale[2] * demand_to_total) / sum(self.rewScale)
else:
reward = 0
return reward
def get_state_value_to_opti(self, pump_speeds):
np.clip(a=pump_speeds,
a_min=self.speedLimitLo,
a_max=self.speedLimitHi,
out=pump_speeds)
for group_id, pump_group in enumerate(self.pumpGroups):
for pump in pump_group:
self.wds.pumps[pump].speed = pump_speeds[group_id]
self.wds.solve()
return self.get_state_value()
def reward_to_scipy(self, pump_speeds):
"""Only minimization allowed."""
return -self.get_state_value_to_opti(pump_speeds)
def reward_to_deap(self, pump_speeds):
"""Return should be tuple."""
return self.get_state_value_to_opti(np.asarray(pump_speeds)),
def update_pump_speeds(self):
for i, pump_group in enumerate(self.pumpGroups):
self.pump_speeds[i] = self.wds.pumps[pump_group[0]].speed
return self.pump_speeds
def get_pump_speeds(self):
self.update_pump_speeds()
return self.pump_speeds
def __init__(self,
wds_name='anytown_master',
speed_increment=.05,
episode_len=10,
pump_groups=[['78', '79']],
total_demand_lo=.3,
total_demand_hi=1.1,
reset_orig_pump_speeds=False,
reset_orig_demands=False,
seed=None):
self.seedNum = seed
if self.seedNum:
np.random.seed(self.seedNum)
else:
np.random.seed()
pathToRoot = os.path.dirname(os.path.realpath(__file__))
pathToWDS = os.path.join(pathToRoot, 'water_networks',
wds_name + '.inp')
self.wds = Network(pathToWDS)
self.demandDict = self.build_demand_dict()
self.pumpGroups = pump_groups
self.pump_speeds = np.ones(shape=(len(self.pumpGroups)),
dtype=np.float32)
self.pumpEffs = np.empty(shape=(len(self.pumpGroups)),
dtype=np.float32)
nomHCurvePtsDict, nomECurvePtsDict = self.get_performance_curve_points(
)
nomHCurvePoliDict = self.fit_polinomials(nomHCurvePtsDict,
degree=2,
encapsulated=True)
self.nomECurvePoliDict = self.fit_polinomials(nomECurvePtsDict,
degree=4,
encapsulated=True)
self.sumOfDemands = sum(
[demand for demand in self.wds.junctions.basedemand])
self.demandRandomizer = self.build_truncnorm_randomizer(lo=.7,
hi=1.3,
mu=1.0,
sigma=1.0)
# Theoretical bounds of {head, efficiency}
peak_heads = []
for key in nomHCurvePoliDict.keys():
max_q = np.max(nomHCurvePtsDict[key][:, 0])
opti_result = minimize(-nomHCurvePoliDict[key],
x0=1,
bounds=[(0, max_q)])
peak_heads.append(nomHCurvePoliDict[key](opti_result.x[0]))
peak_effs = []
for key in nomHCurvePoliDict.keys():
max_q = np.max(nomHCurvePtsDict[key][:, 0])
q_list = np.linspace(0, max_q, 10)
head_poli = nomHCurvePoliDict[key]
eff_poli = self.nomECurvePoliDict[key]
opti_result = minimize(-eff_poli, x0=1, bounds=[(0, max_q)])
peak_effs.append(eff_poli(opti_result.x[0]))
self.peakTotEff = np.prod(peak_effs)
# Reward control
self.dimensions = len(self.pumpGroups)
self.episodeLength = episode_len
self.headLimitLo = 15
self.headLimitHi = 120
self.maxHead = np.max(peak_heads)
self.rewScale = [5, 8, 3] # eff, head, pump
self.baseReward = +1
self.bumpPenalty = -1
self.distanceRange = .5
self.wrongMovePenalty = -1
self.lazinessPenalty = -1
# ----- ----- ----- ----- -----
# Tweaking reward
# ----- ----- ----- ----- -----
#maxReward = 5
# ----- ----- ----- ----- -----
self.maxReward = +1
self.minReward = -1
# Inner variables
self.spec = None
self.metadata = None
self.totalDemandLo = total_demand_lo
self.totalDemandHi = total_demand_hi
self.speedIncrement = speed_increment
self.speedLimitLo = .7
self.speedLimitHi = 1.2
self.validSpeeds = np.arange(self.speedLimitLo,
self.speedLimitHi + .001,
self.speedIncrement,
dtype=np.float32)
self.resetOrigPumpSpeeds = reset_orig_pump_speeds
self.resetOrigDemands = reset_orig_demands
self.optimized_speeds = np.empty(shape=(len(self.pumpGroups)),
dtype=np.float32)
self.optimized_speeds.fill(np.nan)
self.optimized_value = np.nan
self.previous_distance = np.nan
# initialization of {observation, steps, done}
observation = self.reset(training=False)
self.action_space = gym.spaces.Discrete(2 * self.dimensions + 1)
self.observation_space = gym.spaces.Box(
low=-1,
high=+1,
shape=(len(self.wds.junctions) + len(self.pumpGroups), ),
dtype=np.float32)
# for one-shot tests
self.one_shot = rs.rs(target=self.reward_to_deap,
dims=self.dimensions,
limit_lo=self.speedLimitLo,
limit_hi=self.speedLimitHi,
step_size=self.speedIncrement,
maxfev=1)
class TestNetwork(object):
@classmethod
def setup_class(self):
self.network = Network(inputfile="tests/testnetwork.inp")
self.network.solve()
@classmethod
def teadown(self):
self.network.ep.ENclose()
def test01_network(self):
# test0 node count
assert_equal(len(self.network.nodes), 11)
# test0 link count
assert_equal(len(self.network.links), 12)
# test0 reservoir count
assert_equal(len(self.network.reservoirs), 1)
# test0 valve count
assert_equal(len(self.network.valves), 1)
# test0 pump count
assert_equal(len(self.network.pumps), 1)
# test0 tank count
assert_equal(len(self.network.tanks), 1)
def test02_link(self):
# test0 the properties of a single link
link = self.network.links["11"]
# pipe index and uid
assert_equal(link.index, 9)
assert_equal(link.uid, "11")
# from/to node
assert_equal(link.from_node.uid, "4")
assert_equal(link.to_node.uid, "9")
def test03_pipe(self):
# test0 the properties of a single pipe
pipe = self.network.links["11"]
# check type
assert_equal(pipe.link_type, "pipe")
assert_almost_equal(pipe.length, 100, 2)
assert_almost_equal(pipe.diameter, 150, 2)
assert_almost_equal(pipe.roughness, 0.1, 2)
assert_almost_equal(pipe.minorloss, 0.1, 2)
# flow
assert_almost_equal(pipe.flow, 87.92, 2)
# direction
assert_almost_equal(pipe.velocity, 1.38, 2)
# status
assert_equal(pipe.status, 1)
# headloss
assert_almost_equal(pipe.headloss, 1.29, 2)
# upstream/downstream node
assert_equal(pipe.upstream_node.uid, "4")
assert_equal(pipe.downstream_node.uid, "9")
def test04_pump(self):
pump = self.network.pumps["2"]
# check type
assert_equal(pump.link_type, "pump")
assert_equal(pump.speed, 1.0)
assert_almost_equal(pump.flow, 109.67, 2)
# change speed
pump.speed = 1.5
assert_equal(pump.speed, 1.5)
# resolve network
self.network.solve()
assert_almost_equal(pump.flow, 164.5, 2)
# revert speed
pump.speed = 1.0
self.network.solve()
def test05_valve(self):
valve = self.network.valves["9"]
# check type
assert_equal(valve.link_type, "valve")
# check valve type
assert_equal(valve.valve_type, "PRV")
# valve settings
assert_equal(valve.setting, 5)
assert_almost_equal(valve.downstream_node.pressure, 5, 2)
# change setting
valve.setting = 10
assert_equal(valve.setting, 10)
self.network.solve()
assert_almost_equal(valve.downstream_node.pressure, 10, 2)
def test06_node(self):
node = self.network.nodes["4"]
# uid
assert_equal(node.uid, "4")
# coordinates
coordinates = node.coordinates
assert_almost_equal(coordinates[0], 2103.02, 2)
assert_almost_equal(coordinates[1], 5747.69, 2)
# links
assert_equal(len(node.links), 3)
# up and downstream links
assert_equal(len(node.downstream_links), 2)
assert_equal(len(node.upstream_links), 1)
# inflow
assert_equal(round(node.inflow, 2), 109.67)
# outflow
assert_equal(round(node.outflow, 2),
round(node.inflow, 2) - node.demand)
# elevation
assert_equal(node.elevation, 5)
# head
assert_equal(round(node.head, 2), 25.13)
def test07_junction(self):
junction = self.network.junctions["4"]
assert_equal(round(junction.basedemand, 2), 1)
assert_equal(round(junction.demand, 2), 1)
def test08_tank(self):
tank = self.network.tanks["11"]
assert_equal(round(tank.diameter, 2), 50)
assert_equal(round(tank.initvolume, 2), 19634.95)
assert_equal(tank.minvolume, 0)
assert_equal(tank.minlevel, 0)
assert_equal(tank.maxlevel, 20)
assert_equal(round(tank.volume, 2), 19634.95)
assert_equal(round(tank.maxvolume), 2 * round(tank.volume))
def test09_time(self):
junction = self.network.junctions["4"]
self.network.solve(3600)
assert_equal(round(junction.demand, 2), 2)
self.network.solve(7200)
assert_equal(round(junction.demand, 2), 3)
def test10_collections(self):
# collection attributes as pandas Series
assert_almost_equal(self.network.pipes.flow.mean(), 46.78, 2)
assert_almost_equal(self.network.pipes.diameter.max(), 150, 2)
assert_almost_equal(self.network.pipes.velocity.min(), 0.105, 2)
assert_equal(self.network.valves.setting.mean(), 10)
assert_almost_equal(self.network.junctions.demand.mean(), 2.33, 2)
# filtering and slicing collections
assert_equal(len(self.network.pipes[self.network.pipes.velocity > 3]),
3)
assert_equal(len(self.network.nodes[self.network.nodes.pressure < 20]),
5)
#increase the size of all pipes
self.network.pipes.diameter += 500
assert_almost_equal(self.network.pipes.diameter.mean(), 605, 2)
self.network.pipes.diameter -= 500
self.network.solve()
# resize pipes, and recalculate velocity
self.network.pipes[self.network.pipes.velocity > 3].diameter += 100
self.network.solve()
assert_equal(len(self.network.pipes[self.network.pipes.velocity > 3]),
0)
def test11_timeseries(self):
# run network
self.network.run()
# check return types
# should return Series
assert (isinstance(self.network.pipes["1"].velocity, pd.Series))
# should return Dataframe
assert (isinstance(self.network.pipes.velocity, pd.DataFrame))
# timeseries operations
# pipe 1 max velocity
assert_almost_equal(self.network.pipes["1"].velocity.mean(), 1.66, 2)
# all day mean velocity
assert_almost_equal(self.network.pipes.velocity.mean().mean(), 1.14, 2)
# test revert to steady state calculation
self.network.solve()
assert (isinstance(self.network.pipes["1"].velocity, float))
assert (isinstance(self.network.pipes.velocity, pd.Series))
def test12_comments(self):
# test reading comments
assert_equal(self.network.links['1'].comment, "testcommentpipe")
assert_equal(self.network.reservoirs['in'].comment,
"testcommentreservoir")
assert_equal(self.network.tanks['11'].comment, "testcommenttank")
assert_equal(self.network.junctions['2'].comment,
"testcommentjunction")
# test writing comments
self.network.links['1'].comment = 'testwrite'
assert_equal(self.network.links['1'].comment, 'testwrite')
def setup_class(self):
self.network = Network(inputfile="tests/testnetwork.inp")
self.network.solve()
class TestGeneratedNetwork(object):
@classmethod
def setup_class(self):
self.network = Network()
@classmethod
def teadown(self):
self.network.ep.ENclose()
def test00_build_network(self):
network = self.network
network.ep.ENsettimeparam(epanet2.EN_DURATION, 10 * 3600)
# add nodes
reservoir = network.add_reservoir('in', 0, 30)
reservoir.elevation = 10
pattern_values = [1, 2, 3, 4, 5, 4, 3, 2, 1, 1]
pattern = network.add_pattern('1', pattern_values)
junctions = {
'2': (10, 30, 0),
'3': (20, 30, 0),
'4': (30, 30, 1),
'5': (30, 20, 1),
'6': (30, 10, 1),
'7': (40, 10, 1),
'8': (40, 20, 1),
'9': (40, 30, 1),
'10': (50, 30, 1)
}
links = {
'1': ('in', '2'),
'3': ('3', '4'),
'4': ('4', '5'),
'5': ('5', '6'),
'6': ('6', '7'),
'7': ('7', '8'),
'8': ('8', '9'),
'10': ('5', '8'),
'11': ('4', '9'),
'12': ('9', '11')
}
for uid, coord in junctions.items():
node = network.add_junction(uid,
coord[0],
coord[1],
elevation=0,
basedemand=coord[2])
node.pattern = pattern
tank = network.add_tank('11',
40,
40,
diameter=50,
maxlevel=20,
minlevel=0,
tanklevel=10)
for uid, coord in links.items():
link = network.add_pipe(uid,
coord[0],
coord[1],
diameter=100,
length=100,
roughness=0.1)
valve = network.add_valve('9',
'prv',
'9',
'10',
diameter=100,
setting=5)
pump = network.add_pump('2', '2', '3', speed=1)
curve = network.add_curve('1', [(100, 50)])
pump.curve = curve
network.nodes['4'].elevation = 5
network.links['11'].diameter = 150
network.links['11'].minorloss = 0.1
network.solve()
def test01_network(self):
# test0 node count
assert_equal(len(self.network.nodes), 11)
# test0 link count
assert_equal(len(self.network.links), 12)
# test0 reservoir count
assert_equal(len(self.network.reservoirs), 1)
# test0 valve count
assert_equal(len(self.network.valves), 1)
# test0 pump count
assert_equal(len(self.network.pumps), 1)
# test0 tank count
assert_equal(len(self.network.tanks), 1)
def test02_link(self):
# test0 the properties of a single link
link = self.network.links['11']
# pipe index and uid
assert_equal(link.uid, '11')
# from/to node
assert_equal(link.from_node.uid, '4')
assert_equal(link.to_node.uid, '9')
def test03_pipe(self):
# test0 the properties of a single pipe
pipe = self.network.links['11']
# check type
assert_equal(pipe.link_type, 'pipe')
assert_almost_equal(pipe.length, 100, 2)
assert_almost_equal(pipe.diameter, 150, 2)
assert_almost_equal(pipe.roughness, 0.1, 2)
assert_almost_equal(pipe.minorloss, 0.1, 2)
# flow
assert_almost_equal(pipe.flow, 87.92, 2)
# direction
assert_almost_equal(pipe.velocity, 1.38, 2)
# status
assert_equal(pipe.status, 1)
# headloss
assert_almost_equal(pipe.headloss, 1.29, 2)
# upstream/downstream node
assert_equal(pipe.upstream_node.uid, '4')
assert_equal(pipe.downstream_node.uid, '9')
def test04_pump(self):
pump = self.network.pumps['2']
# check type
assert_equal(pump.link_type, 'pump')
assert_equal(pump.speed, 1.0)
assert_almost_equal(pump.flow, 109.67, 2)
# change speed
pump.speed = 1.5
assert_equal(pump.speed, 1.5)
# resolve network
self.network.solve()
assert_almost_equal(pump.flow, 164.5, 2)
# revert speed
pump.speed = 1.0
self.network.solve()
def test05_valve(self):
valve = self.network.valves['9']
# check type
assert_equal(valve.link_type, 'valve')
# check valve type
assert_equal(valve.valve_type, 'PRV')
# valve settings
assert_equal(valve.setting, 5)
assert_almost_equal(valve.downstream_node.pressure, 5, 2)
# change setting
valve.setting = 10
assert_equal(valve.setting, 10)
self.network.solve()
assert_almost_equal(valve.downstream_node.pressure, 10, 2)
def test06_node(self):
node = self.network.nodes['4']
# uid
assert_equal(node.uid, '4')
# coordinates
#coordinates = node.coordinates
# dont test these for created networks
#assert_almost_equal(coordinates[0],2103.02,2)
#assert_almost_equal(coordinates[1],5747.69,2)
# links
assert_equal(len(node.links), 3)
# up and downstream links
assert_equal(len(node.downstream_links), 2)
assert_equal(len(node.upstream_links), 1)
# inflow
assert_equal(round(node.inflow, 2), 109.67)
# outflow
assert_equal(round(node.outflow, 2),
round(node.inflow, 2) - node.demand)
# elevation
assert_equal(node.elevation, 5)
# head
assert_equal(round(node.head, 2), 25.13)
def test07_junction(self):
junction = self.network.junctions['4']
assert_equal(round(junction.basedemand, 2), 1)
assert_equal(round(junction.demand, 2), 1)
def test08_tank(self):
tank = self.network.tanks['11']
assert_equal(tank.diameter, 50)
assert_equal(round(tank.initvolume, 2), 19634.95)
assert_equal(tank.minvolume, 0)
assert_equal(tank.minlevel, 0)
assert_equal(tank.maxlevel, 20)
assert_equal(round(tank.volume, 2), 19634.95)
assert_equal(round(tank.maxvolume), 2 * round(tank.volume))
def test09_time(self):
junction = self.network.junctions['4']
self.network.solve(3600)
assert_equal(round(junction.demand, 2), 2)
self.network.solve(7200)
assert_equal(round(junction.demand, 2), 3)
def test10_collections(self):
# collection attributes as pandas Series
assert_almost_equal(self.network.pipes.flow.mean(), 46.77, 2)
assert_almost_equal(self.network.pipes.diameter.max(), 150, 2)
assert_almost_equal(self.network.pipes.velocity.min(), 0.105, 2)
assert_equal(self.network.valves.setting.mean(), 10)
assert_almost_equal(self.network.junctions.demand.mean(), 2.33, 2)
# filtering and slicing collections
assert_equal(len(self.network.pipes[self.network.pipes.velocity > 3]),
3)
assert_equal(len(self.network.nodes[self.network.nodes.pressure < 20]),
5)
#increase the size of all pipes
self.network.pipes.diameter += 500
assert_almost_equal(self.network.pipes.diameter.mean(), 605, 2)
self.network.pipes.diameter -= 500
self.network.solve()
# resize pipes, and recalculate velocity
self.network.pipes[self.network.pipes.velocity > 3].diameter += 100
self.network.solve()
assert_equal(len(self.network.pipes[self.network.pipes.velocity > 3]),
0)
def test11_timeseries(self):
# run network
self.network.run()
# check return types
# should return Series
assert (isinstance(self.network.pipes['1'].velocity, pd.Series))
# should return Dataframe
assert (isinstance(self.network.pipes.velocity, pd.DataFrame))
# timeseries operations
# pipe 1 max velocity
assert_almost_equal(self.network.pipes['1'].velocity.mean(), 1.66, 2)
# all day mean velocity
assert_almost_equal(self.network.pipes.velocity.mean().mean(), 1.14, 2)
# test revert to steady state calculation
self.network.solve()
print(type(self.network.pipes['1'].velocity))
assert (isinstance(self.network.pipes['1'].velocity, float))
assert (isinstance(self.network.pipes.velocity, pd.Series))
def setup_class(self):
self.network = Network()
def add_tank_get_score(num_junctions, avg_initial_pressure_at_each_junc):
global tank_elev_array
network = Network(inp_file)
junc_elev_array = []
junc_x_y_array = []
junc_id_array = []
score_array = []
# this loop add a tank to Node 1, calculate the peak-demand average pressure, stores it, and deletes the tank
# it then adds a tank to Node 2 and repeats until all nodal locations are scored
for junction in range(num_junctions):
junc_elev_array.append(network.ep.ENgetnodevalue(junction + 1, 0))
junc_x_y_array.append(network.ep.ENgetcoord(junction + 1))
junc_id_array.append(network.ep.ENgetnodeid(junction + 1))
optimum_elevation = junc_elev_array[
junction] + avg_initial_pressure_at_each_junc[junction]
tank_elev_array.append(optimum_elevation)
network.add_tank(uid='balancing_tank',
x=junc_x_y_array[junction][0],
y=junc_x_y_array[junction][1],
elevation=optimum_elevation,
diameter=100,
maxlevel=1000,
minlevel=0,
tanklevel=0)
network.add_pipe(uid='balancing_tank_pipe',
from_node=junc_id_array[junction],
to_node='balancing_tank',
diameter=1000,
length=10,
roughness=1E9,
check_valve=False)
network.save_inputfile(inp_file)
junction_score = score_pressure_array(
solve_and_return_pressures(num_junctions))
score_array.append(junction_score)
print("Junction index ", score_index_counter, " : ", junction_score)
network.delete_link('balancing_tank_pipe')
network.delete_node('balancing_tank')
network.reset()
network.save_inputfile(inp_file)
return score_array
def setup_class(self):
threaded = Network('tests/testnetwork.inp')
self.network = Network()
def setup_class(self):
self.network = Network()
self.network.solve()
class Test_line_network(unittest.TestCase):
# Test simple network consisting of 4 nodes in a line
def setUp(self):
# self.net = Network(inputfile="tests/Case1.inp")
self.net = Network(inputfile="Case1.inp")
self.pp = PhreeqPython()
self.vic = Victoria(self.net, self.pp)
self.solutions = {}
sol0 = self.pp.add_solution({'Ca': 0, 'units': 'mmol/l'})
sol1 = self.pp.add_solution({'Ca': 1, 'units': 'mmol/l'})
self.solutions[0] = sol0
self.solutions[self.net.reservoirs['1'].uid] = sol1
def test1(self):
models = self.vic.solver.models
# Test node count
self.assertEqual(len(self.net.nodes), len(models.nodes))
# Test junction count
self.assertEqual(len(self.net.junctions), len(models.junctions))
# Test reservoir count
self.assertEqual(len(self.net.reservoirs), len(models.reservoirs))
# Test tank count
self.assertEqual(len(self.net.tanks), len(models.tanks))
# Test link count
self.assertEqual(len(self.net.links), len(models.links))
# Test pipe count
self.assertEqual(len(self.net.pipes), len(models.pipes))
# Test pump count
self.assertEqual(len(self.net.pumps), len(models.pumps))
# Test valve count
self.assertEqual(len(self.net.valves), len(models.valves))
def test2(self):
# Fill the network with a standard solution
self.net.solve()
self.vic.fill_network(self.solutions, from_reservoir=False)
# Test the initial concentrations of Ca
self.assertEqual(
self.vic.get_conc_pipe_avg(self.net.links['1'], 'Ca', 'mmol'), 0)
self.assertEqual(
self.vic.get_conc_pipe_avg(self.net.links['2'], 'Ca', 'mmol'), 0)
self.assertEqual(
self.vic.get_conc_pipe_avg(self.net.links['3'], 'Ca', 'mmol'), 0)
self.assertEqual(
self.vic.get_conc_pipe_avg(self.net.links['6'], 'Ca', 'mmol'), 0)
def test3(self):
# Fill the network with the initial reservoir solution
self.net.solve()
self.vic.fill_network(self.solutions, from_reservoir=True)
# Test the initial concentrations of Ca
self.assertAlmostEqual(
self.vic.get_conc_pipe_avg(self.net.links['1'], 'Ca', 'mmol'), 1,
4)
self.assertAlmostEqual(
self.vic.get_conc_pipe_avg(self.net.links['2'], 'Ca', 'mmol'), 1,
4)
self.assertAlmostEqual(
self.vic.get_conc_pipe_avg(self.net.links['3'], 'Ca', 'mmol'), 1,
4)
self.assertAlmostEqual(
self.vic.get_conc_pipe_avg(self.net.links['6'], 'Ca', 'mmol'), 1,
4)
def test4(self):
# Test the residence time in the pipes. Each pipe has a residence time of 1000 seconds
# Time parameters Victoria
time_end = 2 # hours
timestep_network = 60 # minutes
timestep_victoria = 1 # second
# Convert units to seconds
time_end *= 3600
timestep_network *= 60
time_count = 0
# Fill the network
self.net.solve() # Need to run Epynet for a single timestep
self.vic.fill_network(self.solutions, from_reservoir=False)
for t1 in range(0, time_end, timestep_network):
self.net.solve(simtime=t1)
self.vic.check_flow_direction()
for t2 in range(0, timestep_network, timestep_victoria):
self.vic.step(timestep_victoria, self.solutions)
time_count += t2
if time_count == 999:
node = self.vic.get_conc_node(self.net.nodes['2'], 'Ca',
'mmol')
self.assertAlmostEqual(node, 0, 4)
elif time_count == 1000:
node = self.vic.get_conc_node(self.net.nodes['2'], 'Ca',
'mmol')
self.assertAlmostEqual(node, 1, 4)
elif time_count == 1999:
node = self.vic.get_conc_node(self.net.nodes['3'], 'Ca',
'mmol')
self.assertAlmostEqual(node, 0, 4)
elif time_count == 2000:
node = self.vic.get_conc_node(self.net.nodes['3'], 'Ca',
'mmol')
self.assertAlmostEqual(node, 1, 4)
elif time_count == 2999:
node = self.vic.get_conc_node(self.net.nodes['4'], 'Ca',
'mmol')
self.assertAlmostEqual(node, 0, 4)
elif time_count == 3000:
node = self.vic.get_conc_node(self.net.nodes['4'], 'Ca',
'mmol')
self.assertAlmostEqual(node, 1, 4)
elif time_count == 3999:
node = self.vic.get_conc_node(self.net.nodes['6'], 'Ca',
'mmol')
self.assertAlmostEqual(node, 0, 4)
elif time_count == 4000:
node = self.vic.get_conc_node(self.net.nodes['6'], 'Ca',
'mmol')
self.assertAlmostEqual(node, 1, 4)
# load a network
from epynet import Network
import casadi as ca
import numpy as np
import math
# Create a water network model
network = Network("XXX.inp") # Load EPANET file
# Read parameters
node_uid_to_index = {}
for n, node in enumerate(network.nodes):
node_uid_to_index[node.uid] = n
L = np.zeros(len(network.pipes))
C = np.zeros(len(network.pipes))
E = np.zeros(len(network.pipes))
D = np.zeros(len(network.pipes))
Q_nom = np.zeros(len(network.pipes))
for n, pipe in enumerate(network.pipes):
L[n] = pipe.length
D[n] = pipe.diameter / 1000 # Conversion of mm to m
Q_nom[n] = 0.1 # Initial guess
if pipe.roughness > 40:
C[n] = pipe.roughness
else:
E[n] = pipe.roughness * 1e-3
d = np.zeros(len(network.nodes))
for n, junction in enumerate(network.junctions):
d[n] = junction.basedemand / 1000 # Conversion of l/sec to m3/s
