kaklise
commented
4 years ago Are you able to share your script and network model? You can email the files to kaklise@sandia.gov if you would rather not post the files here.
Closed bahadurr closed 4 years ago
kaklise
commented
4 years ago Are you able to share your script and network model? You can email the files to kaklise@sandia.gov if you would rather not post the files here.
bahadurr
commented
4 years ago Katherine
I can share the script (attached) but the network model I cannot because of obvious reasons. The model input file was given to me by Robert Janke from EPA Cincinnati.
I tried the same script with NET6.inp file that is packaged with WNTR and getting the same results. The script can be tested on NET6.inp file if it helps
Rakesh Bahadur, Ph.D., PMP | Leidos MS FS 1-5-1 11951 Freedom Drive Reston, VA 20190 phone: 571.526.7120 bahadurr@leidos.commailto:bahadurr@leidos.com | leidos.com
[cid:image001.png@01CF9B6D.2D0979D0] This email and any attachments to it are intended only for the identified recipients. It may contain proprietary or otherwise legally protected information of Leidos. Any unauthorized use or disclosure of this communication is strictly prohibited. If you have received this communication in error, please notify the sender and delete or otherwise destroy the email and all attachments immediately.
From: Katherine Klise notifications@github.com Sent: Thursday, March 5, 2020 10:13 AM To: USEPA/WNTR WNTR@noreply.github.com Cc: Bahadur, Rakesh [US-US] RAKESH.BAHADUR@leidos.com; Author author@noreply.github.com Subject: EXTERNAL: Re: [USEPA/WNTR] Skeletonization (#128)
Are you able to share your script and network model? You can email the files to kaklise@sandia.govmailto:kaklise@sandia.gov if you would rather not post the files here.
— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHubhttps://github.com/USEPA/WNTR/issues/128?email_source=notifications&email_token=ACNPI4HEYZVH6SNNZUISVPTRF66RPA5CNFSM4LCLXZP2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEN5UKOI#issuecomment-595281209, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ACNPI4EALC76OLUWVI74GMDRF66RPANCNFSM4LCLXZPQ.
bahadurr
commented
4 years ago Katherine
You may not get the attachment as Outlook sometimes strips the python scripts. If that is case then let me know if I can upload the script for you
Rakesh Bahadur, Ph.D., PMP | Leidos MS FS 1-5-1 11951 Freedom Drive Reston, VA 20190 phone: 571.526.7120 bahadurr@leidos.commailto:bahadurr@leidos.com | leidos.com
[cid:image001.png@01CF9B6D.2D0979D0] This email and any attachments to it are intended only for the identified recipients. It may contain proprietary or otherwise legally protected information of Leidos. Any unauthorized use or disclosure of this communication is strictly prohibited. If you have received this communication in error, please notify the sender and delete or otherwise destroy the email and all attachments immediately.
From: Katherine Klise notifications@github.com Sent: Thursday, March 5, 2020 10:13 AM To: USEPA/WNTR WNTR@noreply.github.com Cc: Bahadur, Rakesh [US-US] RAKESH.BAHADUR@leidos.com; Author author@noreply.github.com Subject: EXTERNAL: Re: [USEPA/WNTR] Skeletonization (#128)
Are you able to share your script and network model? You can email the files to kaklise@sandia.govmailto:kaklise@sandia.gov if you would rather not post the files here.
— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHubhttps://github.com/USEPA/WNTR/issues/128?email_source=notifications&email_token=ACNPI4HEYZVH6SNNZUISVPTRF66RPA5CNFSM4LCLXZP2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEN5UKOI#issuecomment-595281209, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ACNPI4EALC76OLUWVI74GMDRF66RPANCNFSM4LCLXZPQ.
kaklise
commented
4 years ago I don't see the attached file, but I can provide an example using Net6 to start the discussion.
The issue might be explained by how demands are extracted from the original and skeletonized model. Within the skeletonization process, base demands and demand patterns are moved to remaining nodes. We can compute total demand at each junction and time using base demands, demand patterns, and the demand multiplier to make sure demands are conserved (using wntr.metrics.expected_demand). If we just extract the base demand (using query_node_attribute), the results will be different because this does not account for the multiple demands and patterns on each junction.
In the following example, Net 6 is skeletonized using a 16 inch pipe diameter threshold. The total expected demand is conserved (130.42 m3/s) but the base demands differ. Does this explain the issue you are seeing?
import wntr
# Create a water network model
orig_wn = wntr.network.WaterNetworkModel('networks/Net6.inp')
print(orig_wn.describe())
# Skeletonize the model using a 16 inch pipe diameter threshold
skel_wn = wntr.morph.skeletonize(orig_wn, 16*0.0254)
print(skel_wn.describe())
# Extract the expected demand for each water network model
# This uses base demand, demand pattern, and the demand multiplier
# Expected demand for both models should be the same
orig_expected_demand = wntr.metrics.expected_demand(orig_wn)
skel_expected_demand = wntr.metrics.expected_demand(skel_wn)
orig_expected_demand.sum(axis=1).plot() # plot timeseries of total demand
skel_expected_demand.sum(axis=1).plot()
print(orig_expected_demand.sum().sum()) # print total demand
print(skel_expected_demand.sum().sum())
# Extract the base demand for each water network model
# The base demand will not be the same
orig_base_demand = orig_wn.query_node_attribute('base_demand')
skel_base_demand = skel_wn.query_node_attribute('base_demand')
print(orig_base_demand.sum()) # print total base demand
print(skel_base_demand.sum())Katherine
I changed the extension of the script from .py to .txt and resending. Hopefully this file won’t be stripped by Outlook. You may want to change the extension back to .y to run it.
My understanding is that the Base demand for the whole model (not individual nodes) should remain same before and after Skeletonization.
Rakesh Bahadur, Ph.D., PMP | Leidos MS FS 1-5-1 11951 Freedom Drive Reston, VA 20190 phone: 571.526.7120 bahadurr@leidos.commailto:bahadurr@leidos.com | leidos.com
[cid:image001.png@01CF9B6D.2D0979D0] This email and any attachments to it are intended only for the identified recipients. It may contain proprietary or otherwise legally protected information of Leidos. Any unauthorized use or disclosure of this communication is strictly prohibited. If you have received this communication in error, please notify the sender and delete or otherwise destroy the email and all attachments immediately.
From: Katherine Klise notifications@github.com Sent: Thursday, March 5, 2020 10:59 AM To: USEPA/WNTR WNTR@noreply.github.com Cc: Bahadur, Rakesh [US-US] RAKESH.BAHADUR@leidos.com; Author author@noreply.github.com Subject: EXTERNAL: Re: [USEPA/WNTR] Skeletonization (#128)
I don't see the attached file, but I can provide an example using Net6 to start the discussion.
The issue might be explained by how demands are extracted from the original and skeletonized model. Within the skeletonization process, base demands and demand patterns are moved to remaining nodes. We can compute total demand at each junction and time using base demands, demand patterns, and the demand multiplier to make sure demands are conserved (using wntr.metrics.expected_demand). If we just extract the base demand (using query_node_attribute), the results will be different because this does not account for the multiple demands and patterns on each junction.
In the following example, Net 6 is skeletonized using a 16 inch pipe diameter threshold. The total expected demand is conserved (130.42 m3/s) but the base demands differ. Does this explain the issue you are seeing?
import wntr
orig_wn = wntr.network.WaterNetworkModel('networks/Net6.inp')
print(orig_wn.describe())
skel_wn = wntr.morph.skeletonize(orig_wn, 16*0.0254)
print(skel_wn.describe())
orig_expected_demand = wntr.metrics.expected_demand(orig_wn)
skel_expected_demand = wntr.metrics.expected_demand(skel_wn)
orig_expected_demand.sum(axis=1).plot() # plot timeseries of total demand
skel_expected_demand.sum(axis=1).plot()
print(orig_expected_demand.sum().sum()) # print total demand
print(skel_expected_demand.sum().sum())
orig_base_demand = orig_wn.query_node_attribute('base_demand')
skel_base_demand = skel_wn.query_node_attribute('base_demand')
print(orig_base_demand.sum()) # print total base demand
print(skel_base_demand.sum())
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import numpy as np import matplotlib.pyplot as plt import pickle import wntr """ The wntr.morph.skel module contains functions to skeletonize water network models. """ import logging import itertools import networkx as nx
from wntr.network.elements import Pipe, Junction from wntr.sim.core import WNTRSimulator from wntr.sim import EpanetSimulator from wntr.morph.node import _deepcopy_wn
logger = logging.getLogger(name)
inp_file = 'C:/Users/bahadurr/Documents/Projects 2019/eCOE/Test Cases/Skeleonization/Texas_Example2_Testing_FOUO.inp' wn = wntr.network.WaterNetworkModel(inp_file)
def skeletonize(wn, pipe_diameter_threshold, branch_trim=True, series_pipe_merge=True, parallel_pipe_merge=True, max_cycles=None, use_epanet=True, return_map=False):
"""
Perform network skeletonization using branch trimming, series pipe merge,
and parallel pipe merge operations. Candidate pipes for removal is based
on a pipe diameter threshold.
Parameters
-------------
wn: wntr WaterNetworkModel
A WaterNetworkModel object
pipe_diameter_threshold: float
Pipe diameter threshold used to determine candidate pipes for skeletonization
branch_trim: bool (optional, default = True)
Include branch trimming in skeletonization
series_pipe_merge: bool (optional, default = True)
Include series pipe merge in skeletonization
parallel_pipe_merge: bool (optional, default = True)
Include parallel pipe merge in skeletonization
max_cycles: int or None (optional, default = None)
Defines the maximum number of cycles in the skeletonization process.
One cycle performs branch trimming for all candidate pipes, followed
by series pipe merging for all candidate pipes, followed by parallel
pipe merging for all candidate pipes. If max_cycles is set to None,
skeletonization will run until the network can no longer be reduced.
use_epanet: bool (optional)
If True, use the EpanetSimulator to compute headloss in pipes. If False,
use the WNTRSimulator to compute headloss in pipes
return_map: bool (optional, default = False)
Return a skeletonization map. The map is a dictionary
that includes original nodes as keys and a list of skeletonized nodes
that were merged into each original node as values.
Returns
--------
A skeletonized WaterNetworkModel object and (if return_map = True) a skeletonization map.
"""
skel = _Skeletonize(wn, use_epanet)
skel.run(pipe_diameter_threshold, branch_trim, series_pipe_merge,
parallel_pipe_merge, max_cycles)
if return_map:
return skel.wn, skel.skeleton_map
else:
return skel.wnclass _Skeletonize(object):
def __init__(self, wn, use_epanet):
# Get a copy of the WaterNetworkModel
self.wn = _deepcopy_wn(wn)
# Get the WaterNetworkModel graph
G = self.wn.get_graph()
G = G.to_undirected()
self.G = G
# Create a map of original nodes to skeletonized nodes
skel_map = {}
for node_name in self.wn.node_name_list:
skel_map[node_name] = [node_name]
self.skeleton_map = skel_map
# Get a list of junction and pipe names that are associated with controls
junc_with_controls = []
pipe_with_controls = []
for name, control in self.wn.controls():
for req in control.requires():
if isinstance(req, Junction):
junc_with_controls.append(req.name)
elif isinstance(req, Pipe):
pipe_with_controls.append(req.name)
self.junc_with_controls = list(set(junc_with_controls))
self.pipe_with_controls = list(set(pipe_with_controls))
# Calculate pipe headloss using a single period EPANET simulation
duration = self.wn.options.time.duration
if use_epanet:
sim = EpanetSimulator(self.wn)
else:
sim = WNTRSimulator(self.wn)
self.wn.options.time.duration = 0
results = sim.run_sim()
head = results.node['head']
headloss = {}
for link_name, link in self.wn.links():
headloss[link_name] = float(abs(head[link.start_node_name] - head[link.end_node_name]))
self.headloss = headloss
self.wn.options.time.duration = duration
self.num_branch_trim = 0
self.num_series_merge = 0
self.num_parallel_merge = 0
def run(self, pipe_threshold, branch_trim=True, series_pipe_merge=True,
parallel_pipe_merge=True, max_cycles=None):
"""
Run iterative branch trim, series pipe merge, and parallel pipe merge
operations based on a pipe diameter threshold.
"""
num_junctions = self.wn.num_junctions
iteration = 0
flag = True
while flag:
if branch_trim:
self.branch_trim(pipe_threshold)
if series_pipe_merge:
self.series_pipe_merge(pipe_threshold)
if parallel_pipe_merge:
self.parallel_pipe_merge(pipe_threshold)
iteration = iteration + 1
if (max_cycles is not None) and (iteration > max_cycles):
flag = False
if num_junctions == self.wn.num_junctions:
flag = False
else:
num_junctions = self.wn.num_junctions
return self.wn, self.skeleton_map
def branch_trim(self, pipe_threshold):
"""
Run a single branch trim operation based on a pipe diameter threshold.
Branch trimming removes dead-end pipes smaller than the pipe
diameter threshold and redistributes demands (and associated demand
patterns) to the neighboring junction.
"""
for junc_name in self.wn.junction_name_list:
if junc_name in self.junc_with_controls:
continue
neighbors = list(nx.neighbors(self.G,junc_name))
if len(neighbors) > 1:
continue
if len(neighbors) == 0:
continue
neigh_junc_name = neighbors[0] # only one neighbor
nPipes = len(self.G.adj[junc_name][neigh_junc_name])
if nPipes > 1:
continue
neigh_junc = self.wn.get_node(neigh_junc_name)
if not (isinstance(neigh_junc, Junction)):
continue
pipe_name = list(self.G.adj[junc_name][neigh_junc_name].keys())[0] # only one pipe
pipe = self.wn.get_link(pipe_name)
if not ((isinstance(pipe, Pipe)) and \
(pipe.diameter <= pipe_threshold) and \
pipe_name not in self.pipe_with_controls):
continue
logger.info('Branch trim: '+ str(junc_name) + str(neighbors))
# Update skeleton map
self.skeleton_map[neigh_junc_name].extend(self.skeleton_map[junc_name])
self.skeleton_map[junc_name] = []
# Move demand
junc = self.wn.get_node(junc_name)
for demand in junc.demand_timeseries_list:
neigh_junc.demand_timeseries_list.append(demand)
junc.demand_timeseries_list.clear()
# Remove node and links from wn and G
self.wn.remove_link(pipe_name)
self.wn.remove_node(junc_name)
self.G.remove_node(junc_name)
self.num_branch_trim +=1
return self.wn, self.skeleton_map
def series_pipe_merge(self, pipe_threshold):
"""
Run a single series pipe merge operation based on a pipe diameter
threshold. This operation combines pipes in series if both pipes are
smaller than the pipe diameter threshold. The larger diameter pipe is
retained, demands (and associated demand patterns) are redistributed
to the nearest junction.
"""
for junc_name in self.wn.junction_name_list:
if junc_name in self.junc_with_controls:
continue
neighbors = list(nx.neighbors(self.G,junc_name))
if not (len(neighbors) == 2):
continue
neigh_junc_name0 = neighbors[0]
neigh_junc_name1 = neighbors[1]
neigh_junc0 = self.wn.get_node(neigh_junc_name0)
neigh_junc1 = self.wn.get_node(neigh_junc_name1)
if not ((isinstance(neigh_junc0, Junction)) or \
(isinstance(neigh_junc1, Junction))):
continue
pipe_name0 = list(self.G.adj[junc_name][neigh_junc_name0].keys())
pipe_name1 = list(self.G.adj[junc_name][neigh_junc_name1].keys())
if (len(pipe_name0) > 1) or (len(pipe_name1) > 1):
continue
pipe_name0 = pipe_name0[0] # only one pipe
pipe_name1 = pipe_name1[0] # only one pipe
pipe0 = self.wn.get_link(pipe_name0)
pipe1 = self.wn.get_link(pipe_name1)
if not ((isinstance(pipe0, Pipe)) and \
(isinstance(pipe1, Pipe)) and \
((pipe0.diameter <= pipe_threshold) and \
(pipe1.diameter <= pipe_threshold)) and \
pipe_name0 not in self.pipe_with_controls and \
pipe_name1 not in self.pipe_with_controls):
continue
# Find closest neighbor junction
if (isinstance(neigh_junc0, Junction)) and \
(isinstance(neigh_junc1, Junction)):
if pipe0.length < pipe1.length:
closest_junc = neigh_junc0
else:
closest_junc = neigh_junc1
elif (isinstance(neigh_junc0, Junction)):
closest_junc = neigh_junc0
elif (isinstance(neigh_junc1, Junction)):
closest_junc = neigh_junc1
else:
continue
logger.info('Series pipe merge: ' + str(junc_name) + str(neighbors))
# Update skeleton map
self.skeleton_map[closest_junc.name].extend(self.skeleton_map[junc_name])
self.skeleton_map[junc_name] = []
# Move demand
junc = self.wn.get_node(junc_name)
for demand in junc.demand_timeseries_list:
closest_junc.demand_timeseries_list.append(demand)
junc.demand_timeseries_list.clear()
# Remove node and links from wn and G
self.wn.remove_link(pipe_name0)
self.wn.remove_link(pipe_name1)
self.wn.remove_node(junc_name)
self.G.remove_node(junc_name)
# Compute new pipe properties
props = self._series_merge_properties(pipe0, pipe1)
# Add new pipe to wn and G
dominant_pipe = self._select_dominant_pipe(pipe0, pipe1)
self.wn.add_pipe(dominant_pipe.name,
start_node_name=neigh_junc_name0,
end_node_name=neigh_junc_name1,
length=props['length'],
diameter=props['diameter'],
roughness=props['roughness'],
minor_loss=props['minorloss'],
status=props['status'])
self.G.add_edge(neigh_junc_name0,
neigh_junc_name1,
dominant_pipe.name)
self.num_series_merge +=1
return self.wn, self.skeleton_map
def parallel_pipe_merge(self, pipe_threshold):
"""
Run a single parallel pipe merge operation based on a pipe diameter
threshold. This operation combines pipes in parallel if both pipes are
smaller than the pipe diameter threshold. The larger diameter pipe is
retained.
"""
for junc_name in self.wn.junction_name_list:
if junc_name in self.junc_with_controls:
continue
neighbors = nx.neighbors(self.G,junc_name)
for neighbor in neighbors:
parallel_pipe_names = list(self.G.adj[junc_name][neighbor].keys())
if len(parallel_pipe_names) == 1:
continue
for (pipe_name0, pipe_name1) in itertools.combinations(parallel_pipe_names, 2):
try:
pipe0 = self.wn.get_link(pipe_name0)
pipe1 = self.wn.get_link(pipe_name1)
except:
continue # one of the pipes removed in previous loop
if not ((isinstance(pipe0, Pipe)) and \
(isinstance(pipe1, Pipe)) and \
((pipe0.diameter <= pipe_threshold) and \
(pipe1.diameter <= pipe_threshold)) and \
pipe_name0 not in self.pipe_with_controls and \
pipe_name1 not in self.pipe_with_controls):
continue
logger.info('Parallel pipe merge: '+ str(junc_name) + str((pipe_name0, pipe_name1)))
# Remove links from wn and G
self.wn.remove_link(pipe_name0)
self.wn.remove_link(pipe_name1)
self.G.remove_edge(neighbor, junc_name, pipe_name0)
self.G.remove_edge(junc_name, neighbor, pipe_name1)
# Compute new pipe properties
props = self._parallel_merge_properties(pipe0, pipe1)
# Add a new pipe to wn and G
dominant_pipe = self._select_dominant_pipe(pipe0, pipe1)
self.wn.add_pipe(dominant_pipe.name,
start_node_name=dominant_pipe.start_node_name,
end_node_name=dominant_pipe.end_node_name,
length=props['length'],
diameter=props['diameter'],
roughness=props['roughness'],
minor_loss=props['minorloss'],
status=props['status'])
self.G.add_edge(dominant_pipe.start_node_name,
dominant_pipe.end_node_name,
dominant_pipe.name)
self.num_parallel_merge +=1
return self.wn, self.skeleton_map
def _select_dominant_pipe(self, pipe0, pipe1):
# Dominant pipe = larger diameter
if pipe0.diameter >= pipe1.diameter:
dominant_pipe = pipe0
else:
dominant_pipe = pipe1
return dominant_pipe
def _series_merge_properties(self, pipe0, pipe1):
props = {}
dominant_pipe = self._select_dominant_pipe(pipe0, pipe1)
props['length'] = pipe0.length + pipe1.length
props['diameter'] = dominant_pipe.diameter
props['minorloss'] = dominant_pipe.minor_loss
props['status'] = dominant_pipe.status
props['roughness'] = (props['length']/(props['diameter']**4.87))**0.54 * \
((pipe0.length/((pipe0.diameter**4.87)*(pipe0.roughness**1.85))) + \
(pipe1.length/((pipe1.diameter**4.87)*(pipe1.roughness**1.85))))**-0.54
return props
def _parallel_merge_properties(self, pipe0, pipe1):
props = {}
dominant_pipe = self._select_dominant_pipe(pipe0, pipe1)
props['length'] = dominant_pipe.length
props['diameter'] = dominant_pipe.diameter
props['minorloss'] = dominant_pipe.minor_loss
props['status'] = dominant_pipe.status
props['roughness'] = ((props['length']**0.54)/(props['diameter']**2.63)) * \
((pipe0.roughness*(pipe0.diameter**2.63))/(pipe0.length**0.54) + \
(pipe1.roughness*(pipe1.diameter**2.63))/(pipe1.length**0.54))
return propswn_model = skeletonize(wn, 16, use_epanet = True) wn_model.write_inpfile('Example2_outwithRB')
kaklise
commented
4 years ago We preserve the original base demand values and demand patterns when combining junctions in the skeletonization process. We should clarify this in the user manual. You could aggregate these into new base demands and demand patterns for each junction, but this could potentially create many new patterns, and makes debugging difficult.
bahadurr
commented
4 years ago Katherine
I think I know the issue. The small script you have sent to me converts pipe diameter from INCHES to METERS. I was using simple inch as there was no mention of the conversion in the manual. It is working fine and giving me reasonable results. I will now compare the original model with the skeletonized model
skel_wn = wntr.morph.skeletonize(orig_wn, 16*0.0254)
Thanks again for all your help
Rakesh Bahadur, Ph.D., PMP | Leidos MS FS 1-5-1 11951 Freedom Drive Reston, VA 20190 phone: 571.526.7120 bahadurr@leidos.commailto:bahadurr@leidos.com | leidos.com
[cid:image001.png@01CF9B6D.2D0979D0] This email and any attachments to it are intended only for the identified recipients. It may contain proprietary or otherwise legally protected information of Leidos. Any unauthorized use or disclosure of this communication is strictly prohibited. If you have received this communication in error, please notify the sender and delete or otherwise destroy the email and all attachments immediately.
From: Katherine Klise notifications@github.com Sent: Thursday, March 5, 2020 10:59 AM To: USEPA/WNTR WNTR@noreply.github.com Cc: Bahadur, Rakesh [US-US] RAKESH.BAHADUR@leidos.com; Author author@noreply.github.com Subject: EXTERNAL: Re: [USEPA/WNTR] Skeletonization (#128)
I don't see the attached file, but I can provide an example using Net6 to start the discussion.
The issue might be explained by how demands are extracted from the original and skeletonized model. Within the skeletonization process, base demands and demand patterns are moved to remaining nodes. We can compute total demand at each junction and time using base demands, demand patterns, and the demand multiplier to make sure demands are conserved (using wntr.metrics.expected_demand). If we just extract the base demand (using query_node_attribute), the results will be different because this does not account for the multiple demands and patterns on each junction.
In the following example, Net 6 is skeletonized using a 16 inch pipe diameter threshold. The total expected demand is conserved (130.42 m3/s) but the base demands differ. Does this explain the issue you are seeing?
import wntr
orig_wn = wntr.network.WaterNetworkModel('networks/Net6.inp')
print(orig_wn.describe())
skel_wn = wntr.morph.skeletonize(orig_wn, 16*0.0254)
print(skel_wn.describe())
orig_expected_demand = wntr.metrics.expected_demand(orig_wn)
skel_expected_demand = wntr.metrics.expected_demand(skel_wn)
orig_expected_demand.sum(axis=1).plot() # plot timeseries of total demand
skel_expected_demand.sum(axis=1).plot()
print(orig_expected_demand.sum().sum()) # print total demand
print(skel_expected_demand.sum().sum())
orig_base_demand = orig_wn.query_node_attribute('base_demand')
skel_base_demand = skel_wn.query_node_attribute('base_demand')
print(orig_base_demand.sum()) # print total base demand
print(skel_base_demand.sum())
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kaklise
commented
4 years ago I'm glad to hear that it's working. WNTR uses SI units throughout the software. https://wntr.readthedocs.io/en/latest/units.html
bahadurr
commented
4 years ago Katherine
I am getting the following Waning:
UserWarning:
Not all curves were used in "C:\WNTR\Testing_Script/Texas_Example2_Testing_FOUO.inp"; added with type None, units conversion left to user
What does it mean?
Thanks again for all your help today
Rakesh Bahadur, Ph.D., PMP | Leidos MS FS 1-5-1 11951 Freedom Drive Reston, VA 20190 phone: 571.526.7120 bahadurr@leidos.commailto:bahadurr@leidos.com | leidos.com
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From: Katherine Klise notifications@github.com Sent: Thursday, March 5, 2020 12:39 PM To: USEPA/WNTR WNTR@noreply.github.com Cc: Bahadur, Rakesh [US-US] RAKESH.BAHADUR@leidos.com; Author author@noreply.github.com Subject: EXTERNAL: Re: [USEPA/WNTR] Skeletonization (#128)
I'm glad to hear that it's working. WNTR uses SI units throughout the software. https://wntr.readthedocs.io/en/latest/units.html
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dbhart
commented
4 years ago Hi Rakesh,
This warning means that for things like energy curves, we don’t use them in WNTR so we leave them alone. We assign curves their properties (pump, energy, etc.) based on what elements they get assigned to (pump-head, tank-volumes). So if there is a curve that never gets used, or which is an energy curve, we put this warning in there so the user knows that there may be a curve we didn’t identify the units for, so we left them alone. Hopefully that makes sense.
Dave H.
I am using WNTRMorphSkel.py script for Skeletonization. The issue is with the aggregation of nodal base demands. Total aggregated base demand for all nodes should remain same even after Skeletonization. But in my test example, the script captures only 10% of the demand. The demands after Skeletonization do not get aggregated and assigned to nodes correctly. I I am using 16 inch pipe diameter for Skeletonization.
Total Demand MGD (aggregate of all nodal base demands) - All pipe model 271.49 MGD Skeltonized Model 30.29 MGD
Thanks for your help