def write(self):
pythonLogger = PythonLogger('admsAplWriter.py')
pythonLogger.postInfoToLogServer('writing to ' + self.address)
#if contains this data, execute the corresponding writing function
with open(self.address, 'w') as file:
self.writeStr(file, HEADER)
self.writeStr(file,SUP)
self.writeTemp(file,MET,[self.data['Met']])
self.writeBdn(file, self.data['Bdn'])
self.writeStr(file,HIL)
self.writeStr(file,FLC)
self.writeTemp(file,GRD,self.data['Grd'])
self.writeStr(file,PUFGAM)
self.writeOpt(file,self.data['Opt'])
self.writeStr(file,CHM)
self.writeStr(file,BKGETC)
#self.writeCoordSys(file) # a special case, to be unified in future
self.writeCoordSys(file, self.data['CoordiSys'])
self.writeStr(file,MAP)
self.writeStr(file,POLD)
#self.writeTemp(self.data['OPT'])
#del self.data['OPT']
self.writeSrc(file, self.data['Src'])
# for category,value in self.data.items():
# self.__getattribute__('write'+category)(file, value)
#to be canceled in future, no copy all
pythonLogger.postInfoToLogServer('finished writing to ' + self.address)
# --Obtain list of building heights-- #
# --Obtain list of building coordinates-- #
listBuildingHeights = []
listBuildingCoordinates = []
for building in listOfIRIs:
buildingHeight = sparqlBuildingHeights(building, sparqlEndPoint)["results"]["bindings"]
height = getBuildingHeights(buildingHeight)
listBuildingHeights.append(height)
buildingCoordinates = sparqlBuildingCoordinates(building, sparqlEndPoint)["results"]["bindings"]
coordinates = getBuildingCoordinates(buildingCoordinates, owlCRS, osmCRS)
listBuildingCoordinates.append(coordinates)
listBuildingsToTransfer = getGeoJSON(listBuildingCoordinates, listBuildingHeights, cityiri)
return json.dumps(listBuildingsToTransfer)
if __name__ == "__main__":
pythonLogger = PythonLogger('ADMSGeoJsonGetter.py')
pythonLogger.postInfoToLogServer('start of ADMSGeoJsonGetter.py')
try:
returnResultsToJava(return_buildings())
pythonLogger.postInfoToLogServer('end of ADMSGeoJsonGetter.py')
except Exception as e:
returnExceptionToJava(e)
pythonLogger.postInfoToLogServer('end of ADMSGeoJsonGetter.py')
from admsAplWriter import admsAplWriter
from collections import namedtuple
from caresjpsutil import PythonLogger
import json
import sys
import config
import cobbling
if __name__ == "__main__":
pythonLogger = PythonLogger('admsMainNew.py')
try:
pythonLogger.postInfoToLogServer('start with ' + sys.argv[0] + ' plant = ' + sys.argv[1] + ' coordinates = ' + sys.argv[2]
+ ' ADMS working dir = ' + sys.argv[3] + ' top = ' + config.bldTopnode)
cobbling.run()
coordinates = str(sys.argv[2]).replace("'", "\"").replace('#',',');
coordinates = json.loads(coordinates)
buildingdata = sys.argv[4].replace("\'","\"")
buildingdata = json.loads(buildingdata)
BDN = namedtuple('BDN', ['BldNumBuildings','BldName','BldType','BldX','BldY','BldHeight', 'BldLength', 'BldWidth', 'BldAngle'])
BDN.BldName = buildingdata['BldName']
BDN.BldNumBuildings = len(BDN.BldName)
BDN.BldType = buildingdata['BldType']
BDN.BldX = buildingdata['BldX']
BDN.BldY = buildingdata['BldY']
BDN.BldHeight = buildingdata['BldHeight']
BDN.BldLength = buildingdata['BldLength']
BDN.BldWidth = buildingdata['BldWidth']
miscCosts, cpo, fame)
u_prices['V_Price_Electricity_001'] /= ex_rates['V_USD_to_SGD']
# Adjust prices to include the transport cost
prices = transport_costs(prices, t_prices)
# Search through the arbitrage opportunities
return look_for_munnies(MoDS_data, prices, dates, s_prices, u_prices)
# Define titles and labels to plot the futures prices data and plot the data
#labels = {'FAME':{'title':'Biodiesel FAME futures prices from Chicago Mercantile Exchange', 'label':'Price (USD per tonne)'},'CPO':{'title':'Crude palm oil futures prices from Chicago Mercantile Exchange', 'label':'Price (USD per tonne)'}, 'x':{'title':'Delivery date (-)', 'label':dates['FAME']}}
#plotting_prices(dates, prices, labels)
if __name__ == "__main__":
pythonLogger = PythonLogger('CPO_to_FAME_MoDS2.py')
pythonLogger.postInfoToLogServer('start of CPO_to_FAME_MoDS2.py')
MoDS_data = str(sys.argv[1])
miscCosts = json.loads(sys.argv[2])
cpo = json.loads(sys.argv[3])
fame = json.loads(sys.argv[4])
try:
returnResultsToJava(run(MoDS_data, miscCosts, cpo, fame))
except Exception as e:
import traceback
pythonLogger.postInfoToLogServer(traceback.print_exc())
returnExceptionToJava(e)
finally:
pythonLogger.postInfoToLogServer('end of CPO_to_FAME_MoDS2.py')
# out4[1], totGen, out0[2], out1[2], out2[2], out3[2], out4[2]]
# industrial cost, renewable generation, residential load 1, residential load 2, residential load 3, commerical load, industrial load]
return [out0, out1, out2, out3, out4, totGen]
if __name__ == "__main__":
import pandas as pd
import sys
from caresjpsutil import returnExceptionToJava, returnResultsToJava
from caresjpsutil import PythonLogger
from datetime import datetime
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
pythonLogger = PythonLogger('system.py')
pythonLogger.postInfoToLogServer('start of system.py, Current Time = ' +
current_time)
folder = sys.argv[1]
#forecast Weather
dfWeather = pd.read_csv(folder + '/WeatherForecast.csv',
sep=',',
header=None)
AirTemp = dfWeather[0].to_numpy()
Radiation = dfWeather[1].to_numpy()
# Residential Agent
# electricity bill structure
household_below = 1 / 50 * 0.01 * np.ones(24)
household_above = 1 / 50 * 0.02 * np.ones(24)
cd = 0.003
Nr = 60 #No of Households?
low = np.asarray(pd.read_csv(folder + '/Pmin.csv', header=None))
high = np.asarray(pd.read_csv(folder + '/Pmax.csv', header=None))
rg.G_ref = G_ref
rg.Eg_ref = Eg_ref
rg.k = k
# solar generation profile
return Ns*rg.solar_energy(Radiation, AirTemp)/1000
if __name__ == "__main__":
import pandas as pd
import sys
from caresjpsutil import returnExceptionToJava, returnResultsToJava
from caresjpsutil import PythonLogger
pythonLogger = PythonLogger('solarRadiation.py')
pythonLogger.postInfoToLogServer('start of solarRadiation.py')
folder = sys.argv[1]
dfWeather=pd.read_csv(folder + '/WeatherForecast.csv', sep=',', header=None)
AirTemp = dfWeather[0].to_numpy()
Radiation = dfWeather[1].to_numpy()
# AirTemp = np.array([28.45, 28.3 , 28.11, 27.9 , 27.74, 27.68, 27.59,
# 27.54, 26.59, 23.82, 26.7 , 28.23, 28.92, 29.15, 30.56, 30.45, 30.65,
# 31.06, 30.97, 30.76, 30.03, 29.76, 29.48, 28.99])
# Radiation = np.array([1.700e-02, 2.100e-02, 1.500e-02, 2.000e-02, 1.800e-02,
# 1.500e-02, 2.400e-02, 3.360e-01, 2.343e+01, 1.193e+02, 4.061e+02, 5.386e+02,
# 6.513e+02, 7.080e+02, 7.000e+02, 6.656e+02, 5.661e+02, 4.238e+02, 2.571e+02,
# 8.630e+01, 1.071e+00, 1.900e-02, 1.800e-02, 1.700e-02])
dfSolar=pd.read_csv(folder + '/PVGenerator.csv', sep=',', header=None)
alpha_sc = dfSolar.iloc[0,0] #0.002132 #<http://www.theworldavatar.com/kb/sgp/singapore/singaporeelectricalnetwork/PVPanel-001.owl#V_TempCoeffSCC_PVPanel-001>
a_ref = dfSolar.iloc[0,1] #1.9136 #<http://www.theworldavatar.com/kb/sgp/singapore/singaporeelectricalnetwork/PVPanel-001.owl#V_IdealityFactor_PVPanel-001>
Ig = np.kron(ip.res.x[ip.Nc:], np.ones(ip.mc))
res_fc = ip.fc.N_time_steps(Ig)
conH2 = res_fc['n']*3.6
genEl = res_fc['P']/1000
return [ip.res.x, ip.objfunction(ip.res.x), ip.report_load(),
np.dot(np.kron(np.identity(24), 1/ip.mc*np.ones(ip.mc)), genH2), np.dot(np.kron(np.identity(24),
1/ip.mc*np.ones(ip.mc)), conH2-genH2), np.dot(np.kron(np.identity(24), 1/ip.mc*np.ones(ip.mc)), conEl-genEl)]
if __name__ == "__main__":
import pandas as pd
import sys
from caresjpsutil import returnExceptionToJava, returnResultsToJava
from caresjpsutil import PythonLogger
pythonLogger = PythonLogger('industrial.py')
pythonLogger.postInfoToLogServer('start of industrial.py')
folder = sys.argv[1]
dfWeather=pd.read_csv(folder + '/WeatherForecast.csv', sep=',', header=None)
AirTemp = dfWeather[0].to_numpy()
industry_below = np.array([0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154, 0.000154])
industry_above = np.array([0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308, 0.000308])
#Electrolyzer
DF_E= pd.read_csv(folder + "/ElectrolyzerConstant.csv", header=None )
# physical constants:
F = DF_E.iloc[2,1] #96485.34 #[A*s/mol] #Faraday's Constant
z1 = DF_E.iloc[6,1] #2 # no. of electrons transferred per reaction #This is standard for the reaction
# reaction information: (25 degreeC and 1 bar @ standard conditions)
dG0 = DF_E.iloc[8,1] #237e3 #[J/mol]
dH = DF_E.iloc[9,1] #286e3 #[J/mol]
# I-U curve parameters:
cb.res.x,
cb.objfunction(cb.res.x),
cb.report_load(),
np.dot(np.kron(np.identity(24), 1 / cb.mc * np.ones(cb.mc)), RoomTemp),
cb.res.x
]
if __name__ == "__main__":
import pandas as pd
import sys
from caresjpsutil import returnExceptionToJava, returnResultsToJava
from caresjpsutil import PythonLogger
pythonLogger = PythonLogger('commercial.py')
pythonLogger.postInfoToLogServer('start of commercial.py')
folder = sys.argv[1]
dfWeather = pd.read_csv(folder + '/WeatherForecast.csv',
sep=',',
header=None)
dfConst = pd.read_csv(folder + '/constant.csv', sep=',', header=None)
AirTemp = dfWeather[0].to_numpy()
Radiation = dfWeather[1].to_numpy()
C1 = dfConst.iloc[0, 0]
C2 = dfConst.iloc[0, 1]
C3 = dfConst.iloc[0, 2]
K1 = dfConst.iloc[0, 3]
K2 = dfConst.iloc[0, 4]
K3 = dfConst.iloc[0, 5]
K4 = dfConst.iloc[0, 6]
K5 = dfConst.iloc[0, 7]
# Preconditions:
# 1. external libraries must be installed in system
# 2. "data" folder must be placed in workingdir of JPS_POWERPLANTEMISSION
# 3. costEmissionCalculation.py file placed in the same folder i.e. caresjpspowerplantemission
# Postconditions:
# Takes in data from csv files and constructs a surrogate model for each of the csv files
# which represents a specific technology
# define a function here that can interpolate the emission and cost surface, and calculate the corresponding power plant emission
# m is the power plant database, n is the emission inventory
if __name__ == "__main__":
pythonLogger = PythonLogger('PlantEmissionCalculationTest.py')
pythonLogger.postInfoToLogServer('start of PlantEmissionCalculationTest.py')
try:
dirPath = sys.argv[1]
# load the powerplant database
df = pd.read_csv(dirPath + '/data/input/powerplant_database.csv', header='infer', sep=',')
# choose ultrasupercritical PC from the database
df_01 = df[df.generation_technology == 'ultrasupercritical']
# load the emission inventory
df_1 = pd.read_csv(dirPath + '/data/input/baseplant/base_ultrasupercritical_PC_coal.csv', header='infer', sep=',')
# choose anthracite and supercritical PC from the database
df_m = df[df.generation_technology == 'supercritical']
df_02 = df_m[df_m.primary_fuel == 'anthracite']
prices["MeOH"][1] = 'Prior Settlement (USD per tonne)'
# Adjust prices to include the transport cost
prices = transport_costs(prices, t_prices)
# Search through the arbitrage opportunities
return look_for_munnies(MoDS_data, prices, dates, s_prices, u_prices)
# Define titles and labels to plot the futures prices data and plot the data
#labels = {'NG':{'title':'Natural gas futures prices from Chicago Mercantile Exchange', 'label':'Price (USD per mmBTU)'},'MeOH':{'title':'Methanol futures prices from Zhengzhou Commodity Exchange', 'label':'Price (USD per tonne)'}, 'x':{'title':'Delivery date (-)', 'label':dates['NG']}}
#plotting_prices(dates, prices, labels)
if __name__ == "__main__":
pythonLogger = PythonLogger('NG_to_MeOH_MoDS.py')
pythonLogger.postInfoToLogServer('start of NG_to_MeOH_MoDS.py')
MoDS_data = str(sys.argv[1])
miscCosts = json.loads(sys.argv[2])
hng = json.loads(sys.argv[3])
zce = json.loads(sys.argv[4])
try:
returnResultsToJava(run(MoDS_data, miscCosts, hng, zce))
except Exception as e:
returnExceptionToJava(e)
finally:
pythonLogger.postInfoToLogServer('end of NG_to_MeOH_MoDS.py')
# run("0.5527777778,-1.9678704734013681E43,-1.947340493583137E67,-1.3064212763820435E47,3063955.568812896,0.0,-1.312550425729447E46,-1.8126031951762418E54,-1.052184254037493E43",
# json.loads('{"V_Price_Electricity_001":"0.0000385833","V_Price_Storage_NaturalGas_001":"0.11104","V_Price_FuelGas_001":"9.8","V_USD_to_CNY":"6.4757816642","V_USD_to_SGD":"1.3582620112","V_Price_Transport_USGC-NEA_NaturalGas_001":"6.571428571_1.142857143","V_Price_Transport_SG-SC_Methanol_001":"22.9","V_Price_CoolingWater_001":"0.00174","V_Price_ProcessWater_001":"0.00033","V_Price_MediumPressureSteam_001":"0.01264","V_Price_HighPressureSteam_001":"0.00349","V_Price_Storage_Methanol_001":"0.2075"}'),
class admsInputDataRetriever(object):
BDN = namedtuple('BDN', [
'BldNumBuildings', 'BldName', 'BldType', 'BldX', 'BldY', 'BldHeight',
'BldLength', 'BldWidth', 'BldAngle'
])
OPT = namedtuple('OPT', [
'OptNumOutputs', 'OptPolName', 'OptInclude', 'OptShortOrLong',
'OptSamplingTime', 'OptSamplingTimeUnits', 'OptCondition',
'OptNumPercentiles', 'OptNumExceedences', 'OptPercentiles',
'OptExceedences', 'OptUnits', 'OptGroupsOrSource', 'OptAllSources',
'OptNumGroups', 'OptIncludedGroups', 'OptIncludedSource',
'OptCreateComprehensiveFile'
])
def __init__(self,
topnode,
bdnnode=None,
range=None,
pollutants=['HC'],
srcLimit=5,
bdnLimit=5,
filterSrc=False,
rawBdn=None):
'''constructor
inputs:
range - user input range {'xmin', 'xmax', 'ymin', 'ymax'}, actual range is the min(user range, region envelope(e.g. jurongisland))
topnode - uri of topnode to begin search within tree struct,
filtersrc - true if use all children under topnode as src, false to use topnode as src directly
bdnnode - top/colleciton node of building
pollutants: pollutant to test
srcLimit: limit of src number, actual number might fewer
bdnLimit: limit of bdn number, actual number might fewer
'''
self.address = None
self.pollutants = pollutants
self.topnode = topnode
self.bdnnode = bdnnode
self.srcLimit = srcLimit
self.bdnLimit = bdnLimit
# TODO-AE remove filterSrc and topnode
self.filterSrc = False
self.rawBdn = rawBdn
self.range = self.getRange(range)
print(self.range)
self.pythonLogger = PythonLogger('admsInputDataRetrieverNew.py')
def getRange(self, userrange):
return ((float(userrange['xmin']), float(userrange['xmax'])),
(float(userrange['ymin']), float(userrange['ymax'])))
def getSrcData(self):
'''get all sourced data :
returns: data object
'''
self.pythonLogger.postInfoToLogServer('start getSrcData(), topnode=' +
self.topnode)
filtered = (self.topnode, )
s = set() #make a set of substance to query later
result = []
for uri in filtered:
print("connecting: {:s}".format(uri))
self.connectDB(uri, connectType='parse')
qdata = self.query("""
PREFIX sys: <http://www.theworldavatar.com/ontology/ontocape/upper_level/system.owl#>
PREFIX space_and_time_extended: <http://www.theworldavatar.com/ontology/ontocape/supporting_concepts/space_and_time/space_and_time_extended.owl#>
PREFIX plant:<http://www.theworldavatar.com/ontology/ontocape/chemical_process_system/CPS_realization/plant.owl#>
PREFIX topology:<http://www.theworldavatar.com/ontology/meta_model/topology/topology.owl#>
PREFIX behavior: <http://www.theworldavatar.com/ontology/ontocape/chemical_process_system/CPS_behavior/behavior.owl#>
PREFIX chemical_process_system:<http://www.theworldavatar.com/ontology/ontocape/chemical_process_system/chemical_process_system.owl#>
PREFIX phase_system:<http://www.theworldavatar.com/ontology/ontocape/material/phase_system/phase_system.owl#>
PREFIX material: <http://www.theworldavatar.com/ontology/ontocape/material/material.owl#>
PREFIX substance:<http://www.theworldavatar.com/ontology/ontocape/material/substance/substance.owl#>
SELECT ?o ?height ?diameter ?content ?x ?y ?velocity ?massflow ?temp ?moleweight ?heatcapa ?density
WHERE {{
?o plant:hasHeight ?he.
?he sys:numericalValue ?height .
?o sys:hasSubsystem ?chm.
?chm plant:hasInsideDiameter ?de . #?dev sys:hasValue ?de.
?de sys:numericalValue ?diameter.
?phase phase_system:has_temperature ?tempE.
?tempE sys:hasValue ?vte.
?vte sys:numericalValue ?temp .
?o space_and_time_extended:hasGISCoordinateSystem ?coe .
?coe space_and_time_extended:hasProjectedCoordinate_x ?xe.
?xe sys:hasValue ?xv.
?xv sys:numericalValue ?x.
?coe space_and_time_extended:hasProjectedCoordinate_y ?ye.
?ye sys:hasValue ?yv.
?yv sys:numericalValue ?y.
?stream topology:leaves ?o.
?stream chemical_process_system:refersToGeneralizedAmount ?ga.
?ga sys:hasSubsystem ?ma.
?ma sys:hasProperty ?ve.
?ve a behavior:Velocity .
?ve sys:hasValue ?vv.
?vv sys:numericalValue ?velocity.
?ma sys:hasProperty ?me.
?me a behavior:ConvectiveMassFlowrate .
?me sys:hasValue ?mv.
?mv sys:numericalValue ?massflow.
?mw a substance:MolecularWeight.
?mw sys:hasValue ?mwv.
?mwv sys:numericalValue ?moleweight.
?cp a phase_system:ThermodynamicStateProperty.
?cp sys:hasValue ?cpv.
?cpv sys:numericalValue ?heatcapa.
?den a phase_system:Density.
?den sys:hasValue ?denv.
?denv sys:numericalValue ?density.
}}
LIMIT 1
""")
aresult, = [row.asdict() for row in qdata]
qdataC = self.query("""
PREFIX sys: <http://www.theworldavatar.com/ontology/ontocape/upper_level/system.owl#>
PREFIX substance:<http://www.theworldavatar.com/ontology/ontocape/material/substance/substance.owl#>
SELECT DISTINCT ?content
WHERE {{
?mix a substance:Mixture.
?mix sys:containsDirectly ?content.
}}
""")
contents = [row['content'].toPython() for row in qdataC]
s = s.union(contents)
aresult['content'] = contents
qdataERate = self.query("""
PREFIX sys: <http://www.theworldavatar.com/ontology/ontocape/upper_level/system.owl#>
PREFIX substance:<http://www.theworldavatar.com/ontology/ontocape/material/substance/substance.owl#>
PREFIX behavior:<http://www.theworldavatar.com/ontology/ontocape/chemical_process_system/CPS_behavior/behavior.owl#>
SELECT DISTINCT ?er ?v
WHERE {{
?mix a substance:Mixture.
?mix sys:hasProperty ?er.
?er a behavior:ConvectiveMassFlowrate.
?er sys:hasValue ?erv.
?erv sys:numericalValue ?v
}}
""")
emissionrates = {
row['er'].toPython(): row['v'].toPython()
for row in qdataERate
}
#todo: sort emissionrates to order of substances
sorteder = []
for content in contents:
name = content.split('#')[1]
for ername, v in emissionrates.items():
if name in ername:
sorteder.append(v)
print(sorteder)
aresult['emissionrates'] = sorteder
result.append(aresult)
print("FILTERED :")
print(result)
'''
#use this if need query substance separately
#query substance for substance related data
cMap = {}
#hard coding for now before I get a better solusion
self.connectDB("http://www.theworldavatar.com/ontology/ontocape/material/substance/substance.owl", connectType = 'parse')
template = """
PREFIX sys: <http://www.theworldavatar.com/ontology/ontocape/upper_level/system.owl#>
PREFIX space_and_time_extended: <http://www.theworldavatar.com/ontology/ontocape/supporting_concepts/space_and_time/space_and_time_extended.owl#>
PREFIX sub:<http://www.theworldavatar.com/ontology/ontocape/material/substance/substance.owl#>
PREFIX behavior: <http://www.theworldavatar.com/ontology/ontocape/chemical_process_system/CPS_behavior/behavior.owl#>
PREFIX phase_system:<http://www.theworldavatar.com/ontology/ontocape/material/phase_system/phase_system.owl#>
SELECT DISTINCT ?o ?hc ?d ?mw
WHERE {{
<{0}> behavior:hasHeatCapacityRatio ?hce. #heatcapacity ratio
?hce sys:hasValue ?hcv .
?hcv sys:numericalValue ?hc.
<{0}> phase_system:has_density ?de . #density
?de sys:hasValue ?dv .
?dv sys:numericalValue ?d.
<{0}> sys:hasProperty ?mwe. #molecular weight
?mwe a sub:MolecularWeight .
?mwe sys:hasValue ?mwv .
?mwv sys:numericalValue ?mw.
}}
LIMIT 1
"""
for sub in s:
print (sub)
print (template.format(sub))
cMap[sub], = self.query(template.format(sub))
#todo:construct an array of contents, and corresponding content data
subData = {}
subData['mw'] = [cMap[content]['mw'] for content in src['content'] ]
subData['d'] = [cMap[content]['d'] for content in src['content'] ]
subData['hc'] = [cMap[content]['hc'] for content in src['content'] ]
pollutantnames = [self.polIRI2Name(content) for content in src['content'] ]
print(subData)
print('!!!!!!!!!!!')
print(src['y'].datatype)
print(src['y'].value)
print(type(src['y'].value))
print(src['y'].toPython)
print(float(src['y']))
'''
packed = []
for src in result:
SrcNumPollutants = len(src['content'])
pollutantnames = [
self.polIRI2Name(content) for content in src['content']
]
newSrc = admsSrc(SrcName=src['o'].toPython(),
SrcHeight=src['height'].toPython(),
SrcDiameter=src['diameter'].toPython(),
SrcVertVeloc=src['velocity'].toPython(),
SrcPolEmissionRate=src['emissionrates'],
SrcPollutants=pollutantnames,
SrcTemperature=src['temp'].toPython(),
SrcX1=src['x'].toPython(),
SrcY1=src['y'].toPython(),
SrcMolWeight=src['moleweight'].toPython(),
SrcDensity=src['density'].toPython(),
SrcSpecHeatCap=src['heatcapa'].toPython(),
SrcNumPollutants=SrcNumPollutants,
SrcMassFlux=src['massflow'].toPython())
packed.append(newSrc)
return packed
def filterBdnEnvelope(self):
'''
Get all buildings within range by comparing range with envelope
return list of building url
'''
xRange, yRange = self.range
qb = self.query('''
PREFIX sys: <http://www.theworldavatar.com/ontology/ontocape/upper_level/system.owl#>
PREFIX citygml: <http://www.theworldavatar.com/ontology/ontocitygml/OntoCityGML.owl#>
PREFIX space_and_time_extended: <http://www.theworldavatar.com/ontology/ontocape/supporting_concepts/space_and_time/space_and_time_extended.owl#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT distinct ?bdn
WHERE{{
?cityM a citygml:CityModelType.
?cityM citygml:boundedBy ?envelope .
?envelope a citygml:EnvelopeType. # get all envelopes
?envelope citygml:upperCornerPoint ?upoint. # get bounds of envelope
?upoint space_and_time_extended:hasGISCoordinateSystem ?uco.
?uco space_and_time_extended:hasProjectedCoordinate_x ?uxe.
?uxe sys:hasValue ?uxv.
?uxv sys:numericalValue ?ux.
?uco space_and_time_extended:hasProjectedCoordinate_y ?uye.
?uye sys:hasValue ?uyv.
?uyv sys:numericalValue ?uy.
?envelope citygml:lowerCornerPoint ?lpoint.
?lpoint space_and_time_extended:hasGISCoordinateSystem ?lco.
?lco space_and_time_extended:hasProjectedCoordinate_x ?lxe.
?lxe sys:hasValue ?lxv.
?lxv sys:numericalValue ?lx.
?lco space_and_time_extended:hasProjectedCoordinate_y ?lye.
?lye sys:hasValue ?lyv.
?lyv sys:numericalValue ?ly.
?cityM citygml:cityObjectMember ?bdn . #get bdn belongs to filterd envelope
Filter(xsd:double(?ly) > "{1}"^^xsd:double && xsd:double(?uy) < "{2}"^^xsd:double && xsd:double(?lx) > "{3}"^^xsd:double && xsd:double(?ux) < "{4}"^^xsd:double) #filter envelope within range
}}
LIMIT {0} #limit of building num
'''.format(self.bdnLimit, *yRange, *xRange))
########todo: in future delete stud data
return tuple(row['bdn'] for row in qb)
def coreBdn2Src(self):
self.pythonLogger.postInfoToLogServer(
'calculate main building for each src')
#compare src coords to each bdn
y_midpoint = (self.range[1][0] + self.range[1][1]) / 2
x_midpoint = (self.range[0][0] + self.range[0][1]) / 2
for src in self.rawSrc:
closed, dClosed, first = None, sys.maxsize, True
#print('find closed bdn for src: '+src.SrcName+" with x: "+str(src.SrcX1) +" y: "+str(src.SrcY1))
self.pythonLogger.postInfoToLogServer('find closed bdn for src: ' +
src.SrcName + " with x: " +
str(src.SrcX1) + " y: " +
str(src.SrcY1) +
", no of buildings=" +
str(len(self.rawBdn.BldX)))
for i in range(len(self.rawBdn.BldX)):
#print('bdn x: ' +str( self.rawBdn.BldX[i]))
print(type(self.rawBdn.BldX[i]))
print(type(src.SrcX1))
#dx, dy = self.rawBdn.BldX[i] - src.SrcX1, self.rawBdn.BldY[i] - src.SrcY1
dx, dy = self.rawBdn.BldX[i] - x_midpoint, self.rawBdn.BldY[
i] - y_midpoint
d = dx * dx + dy * dy
if first:
dClosed = d
closed = self.rawBdn.BldName[i]
first = False
#print('d:{0} dclosed:{1}'.format(d, dClosed))
if d - dClosed < 0:
closed = self.rawBdn.BldName[i]
dClosed = d
print('new smallest distance: ' + str(dClosed))
if closed is not None:
src.setMainBuilding(closed)
else: #err handling, something is wrong if no closed building is found, just throw it
raise Exception(
'Dear lord, no closed buildinf found for src: ' +
src.SrcName)
def getOpt(self, PolNames, SrcNames):
numPol = len(PolNames)
return self.OPT(numPol, PolNames, [1] * numPol, [0] * numPol,
[1] * numPol, [3] * numPol, [0] * numPol, [0] * numPol,
[0] * numPol, [0] * 80, [0] * 80, ['ug/m3'] * numPol,
1, 0, 1, "Grouptank001", SrcNames, 0)
def polIRI2Name(self, polIRI):
substances = {
#'http://www.theworldavatar.com/ontology/ontocape/material/substance/substance.owl#chlorine':'Cl2',
'http://www.theworldavatar.com/ontology/ontocape/material/substance/chemical_species.owl#Nitrogen__dioxide':
'NO2',
'http://www.theworldavatar.com/ontology/ontocape/material/substance/chemical_species.owl#Carbon__monoxide':
'CO',
'http://www.theworldavatar.com/ontology/ontocape/material/substance/chemical_species.owl#Carbon__dioxide':
'CO2',
'http://www.theworldavatar.com/ontology/ontocape/material/substance/pseudocomponent.owl#Unburned_Hydrocarbon':
'HC',
'http://www.theworldavatar.com/ontology/ontocape/material/substance/pseudocomponent.owl#Nitrogen__oxides':
'NOx'
}
if polIRI in substances.keys():
print('Found: ' + substances[polIRI])
return substances[polIRI]
else:
print(polIRI + 'Not found !!!!')
#raise Exception('This substance is not defined!!!!')
return None
def getWeather(self):
'''
get weather data,
for now we trigger a python script to write the .met file directly
'''
#finish writing met
metpath = "C://JPS_DATA/workingdir/JPS/ADMS/test.met"
self.pythonLogger.postInfoToLogServer('path for adms met file=' +
metpath)
return metpath
def get(self):
'''main function, get all related info for adms
returns: complete data for adms
'''
#get all src data
self.rawSrc = self.getSrcData()
##todo: think about this case: when nothing is found ,where should we handle it?
##then nothing should be written, and catch this exception and mute it in main function
if self.rawSrc is None:
raise Exception("No src in found to requiries")
print('raw building: ')
print(self.rawBdn)
rawOpt = self.getOpt(self.pollutants, [s.SrcName for s in self.rawSrc])
self.coreBdn2Src()
#for debuging, in future,define this for data type, i dont think it auto rpint for objet
for src in self.rawSrc:
print(src)
met = self.getWeather()
xran, yran = self.range
grd = xran[0], yran[0], xran[1], yran[1]
#return {'Src': self.rawSrc, 'Bdn': self.rawBdn, 'Opt': rawOpt, 'Met': met, 'Grd':grd}
return {'Src': self.rawSrc, 'Opt': rawOpt, 'Met': met, 'Grd': grd}
def queryEndpoint(self, str):
def literal2TLit(sparqlres):
if 'results' not in sparqlres:
return
for row in sparqlres['results']['bindings']:
for name, value in row.items():
if value['type'] == 'literal' and 'datatype' in value:
value['type'] = 'typed-literal'
print('requesting @ ' + self.address + " with query:")
#print(str)
resp = requests.get(self.address,
params={'query': str},
timeout=1500,
headers={'user-agent': 'my-app/0.0.1'})
print('raw resp:')
print(resp.json())
result = resp.json()
literal2TLit(result)
print('literal to typed-literal')
print(result)
qres = jsresult.JSONResult(result) #json decoded
print('after parse:')
print(qres)
return qres
def queryLocalGraph(self, str):
qres = self.g.query(str)
return qres
def Uri2Local(uri):
'''replace a uri to local address
inputs:
uri - uri to be changed
returns: string - local address
'''
return uri.replace("http://www.jparksimulator.com",
config.root).replace(
"http://www.theworldavatar.com", config.root)
def remote2local(self, func):
'''decorator to change connection function to local connection by replacing given iri to local address
'''
def functionWrapper(self, address):
address = self.Uri2Local(address)
func(self, address)
return functionWrapper
def connectDB(self, address, connectType='endpoint'):
'''connect to db anyhow (we use rdflib graph parse now)
'''
def connectDBActual(address):
'''
Actual method to connect to db
'''
#obsolete: use rdflib locally
self.address = address
if connectType is 'parse':
self.g = rdflib.Graph() #comment out in future
self.g.parse(address) #comment out in future
self.qmethodMap = {
'parse': self.queryLocalGraph,
'endpoint': self.queryEndpoint
}
if not sameGraph(address, self.address):
print('parsing graph: ' + address)
if connectType not in self.qmethodMap:
raise exception('db connection method not defined')
#self.connectType = connectType
self.query = self.qmethodMap[connectType]
connectDBActual(address)
if __name__ == "__main__":
#automatically assigned
household_below = 1 / 50 * 0.01 * np.ones(24)
household_above = 1 / 50 * 0.02 * np.ones(24)
#read bcap from csv
cd = 0.003
Nr = 60
import pandas as pd
import sys
from caresjpsutil import returnExceptionToJava, returnResultsToJava
from caresjpsutil import PythonLogger
pythonLogger = PythonLogger('residential.py')
pythonLogger.postInfoToLogServer('start of residential.py')
folder = sys.argv[1]
low = pd.read_csv(folder + '/Pmin.csv', header=None)
high = pd.read_csv(folder + '/Pmax.csv', header=None)
unwill = pd.read_csv(folder + '/unwill.csv', header=None)
bcap = pd.read_csv(folder + '/bcap.csv', header=None)
appl1 = pd.read_csv(folder + '/ApplianceScheduleLoad1.csv',
sep=',',
header=None)
f = open(folder + "/residence.csv", 'ab')
for r in range(0, 3):
bcap1 = bcap.iloc[r, 0]
seq = appl1.iloc[r]
sche1 = np.array([seq[i:i + 11] for i in range(0, len(seq), 11)])
flex1 = np.array(sche1, dtype=bool)