def test_running_with_nonsensical_swmm_input_file(self):
""" should raise a SWMM5Error exception"""
self.assertRaises(SWMM5Error,SWMM5Simulation,"foo-bar-bat-box.inp")
try:
SWMM5Simulation("foo-bar-bat-box.inp")
pass
except SWMM5Error as e:
pass
self.assertEqual(str(e),'67: \n ERROR 303: cannot open input file.')
def runswmm(model1, lidname, runParamList, swmmInpFile, simulation_date,
sub_i):
# read inp file and change Lid's ksat value
# Write out the model to a new file because SWMM must have a file
Catchment = ['UrbanLand', 'UrbanComplex', 'Manor']
subcat = Catchment[sub_i]
f = open('SWMM_modified.inp', 'w')
swmmInputFileStr = model1.output()
f.write(swmmInputFileStr
) # write out the swmmInputFileStr for modified problem
f.close()
# show the Impervious Treated Percentage of GI in subwatersheds
imperv_i = [0, 0, 0]
imperv_i[0] = swmmInputFileStr.find('UrbanLand\t' + lidname + '\t')
imperv_i[1] = swmmInputFileStr.index('UrbanComplex\t' + lidname + '\t')
imperv_i[2] = swmmInputFileStr.index('Manor\t' + lidname + '\t')
imperv_treated = [0, 0, 0]
for i in range(0, 3):
lineList = swmmInputFileStr[imperv_i[i]:].split(
'\n', 1) ## lists of lines from .inp, split by '\n'
wordList = lineList[0].split()
imperv_treated[i] = wordList[6]
# print('impervious treated = ', imperv_treated)
# Run the new model file
startTime = datetime.now() # obtain the starting time of the run
# startTimeStr = str(startTime)
st = SWMM5Simulation("SWMM_modified.inp", "SWMM_modified.txt")
endTime = datetime.now() # obtain the ending time of the run
elapsedTime = endTime - startTime
minAndSec = divmod(elapsedTime.total_seconds(), 60)
elapsedTimeStr = "%s min, %0.2f sec" % minAndSec
area = runParamList[sub_i]['Area']
N_GI = runParamList[sub_i]['Number']
print(elapsedTimeStr)
(peak, volume, lidDict) = read_report("SWMM_modified.txt")
if lidname == "RainBarrel" and N_GI > 0:
vol_in = (float(lidDict[subcat + ' ' + lidname]['Total Inflow']) -
float(lidDict[subcat + ' ' + lidname]['Surface Outflow']))
vol_re_MG = vol_in / 12.0 * area * N_GI * 7.48 / 1000000 # runoff reduction
volume = volume - vol_re_MG
run = {
"peak": peak,
"volume": volume,
"lidDict": lidDict,
"runParamList": runParamList,
"imperv_treated": imperv_treated
}
return (run)
def runSWMM1(self):
"""Runs swmm and obtain some results"""
ss = SWMM5Simulation("swmm5/examples/simple/swmm5Example.inp")
self.assertEquals(ss.Flow_Units(), 'LPS')
self.assertEquals(ss.entityList(), ['SUBCATCH', 'NODE', 'LINK', 'SYS'])
g = ss.Results('SYS', 'SYS', 1)
[next(g) for x in range(7)]
self.assertAlmostEqual(next(g), 7.600000858306885)
self.assertAlmostEqual(next(g), 8.000000000000000)
ss.getFiles()
return
def replace_and_run(values):
# Open the template file
with open("small_case.template", "r") as inf:
txt = inf.read()
# replace template variable
result = Template(txt).substitute(a1imp=value)
#write the result to a INP file
with open("case.inp", "w") as ouf:
ouf.write(result)
res = SWMM5Simulation("case.inp")
maxfl = max(list(res.Results('NODE', 'N1', 4)))
return maxfl
def runSWMM2(self):
"""Runs swmm and obtain some results"""
ss = SWMM5Simulation("swmm5/examples/waterquality/Example5-EXP.inp")
self.assertEquals(ss.Flow_Units(), 'CFS')
self.assertEquals(ss.entityList(), ['SUBCATCH', 'NODE', 'LINK', 'SYS'])
g = ss.Results('SUBCATCH', 'S3', 8)
self.assertAlmostEqual(next(g), 0.00)
self.assertAlmostEqual(next(g), 0.00)
self.assertAlmostEqual(next(g), 0.00)
self.assertAlmostEqual(next(g), 0.00)
self.assertAlmostEqual(next(g), 0.00)
self.assertAlmostEqual(next(g), 13.44666862487793)
self.assertAlmostEqual(next(g), 14.10814380645752)
self.assertAlmostEqual(next(g), 14.707027435302734)
self.assertAlmostEqual(next(g), 15.237997055053711)
ss.getFiles()
return
def upload2(self, ufile):
# Either save the file to the directory where server.py is
# or save the file to a given path:
# upload_path = '/path/to/project/data/'
upload_path = os.path.dirname(__file__)
# Save the file to a predefined filename
# or use the filename sent by the client:
# upload_filename = ufile.filename
upload_filename = 'saved.txt'
upload_file = os.path.normpath(
os.path.join(upload_path, upload_filename))
upload_file = '../data/saved.inp'
size = 0
with open(upload_file, 'wb') as out:
while True:
data = ufile.file.read(8192)
if not data:
break
out.write(data)
size += len(data)
import pdb;pdb.set_trace()
st=SWMM5Simulation(upload_file)
version = st.SWMM5_Version()
nodes = st.SWMM_Nnodes
out = '''
File received.
Filename: {}
Length: {}
Mime-type: {}
SVMM-version : {}
''' .format(ufile.filename, size, ufile.content_type, version, data)
return out
def run_swmm(self):
if self.working_dir != None:
os.chdir(self.working_dir)
try:
self.st = SWMM5Simulation(self.swmm_filename) # run a simulation
return True
except Exception as e:
print(str(e))
if str(e).strip() == '303:':
print(
str(e) +
' Arquivo {} nao encontrado.'.format(self.swmm_filename))
if str(e).strip() == '317:' or str(e).strip() == '313:':
print(
str(e) +
' Erro ao carregar dados {}.'.format(self.swmm_filename))
if self.temp_file:
os.remove(self.swmm_filename)
sys.exit()
finally:
if self.working_dir != None:
os.chdir(self.root_dir)
def index(self):
st = SWMM5Simulation('../data/CS_H.inp')
import pdb
pdb.set_trace()
return 'OKK'
rep = open('./data/CS_H.rpt', 'r')
Lines = rep.readlines()
count = 0
html = ""
# Strips the newline character
for line in Lines:
if line.strip() == "LID Control Summary":
prt = 1
elif line.strip() == "":
prt = 0
if prt:
html = html + line.strip() + '<br />'
return html
def simulazione(self,
schema_id='',
imp='15',
regime='CAM',
anni='2',
drwh='',
convpp='',
convtv='',
callback='',
_=''):
self.callback = callback
inpPath = "/apps/trigeau/data"
self.result_id = datetime.today().strftime("%Y%m%d%H%M%S")
#self.path = inpPath
#se drwh richiamo la stesa funzione per modificare il drwh
#if drwh and drwh!="DRWH_OK":
#apro il file di base e sostituisco i valori
if drwh == 'SI':
if int(imp) < 30:
return self.render(result=dict(
success=0,
message='Superficie impermeabile deve essere > 15%'))
dxresult = self.simulazione(schema_id=schema_id,
imp=imp,
regime=regime,
anni=anni,
drwh='DX',
callback=callback)
#drwh='DX'
#print 'DRWH SI'
fileName = "%s/%s.inp" % (inpPath, schema_id)
elif drwh == 'DX':
#print 'DRWH DX'
fileName = "%s/%s_drwh.inp" % (inpPath, schema_id)
imp = int((int(imp) - 30) / 0.7)
else:
#print 'DRWH NO'
fileName = "%s/%s.inp" % (inpPath, schema_id)
zona = "Chicago"
rsRandom = randomString()
#mi calcolo l'extent
ret = self.getExtent(schema_id=schema_id)
xCenter = 0
yCenter = 0
if ret:
xCenter = ret["x"]
yCenter = ret["y"]
sxInpFile = "/tmp/%s_%s_sx.inp" % (schema_id, rsRandom)
dxInpFile = "/tmp/%s_%s_dx.inp" % (schema_id, rsRandom)
llsx = []
lldx = []
leadUsage = [] #array co i leadusage calcolati
html = ''
fileInp = open(fileName, 'r')
lines = fileInp.readlines()
index = 0
for line in lines:
if line.strip() == '[RAINGAGES]':
idxRainGages = index
if line.strip() == '[SUBCATCHMENTS]':
idxSubCatchments = index
if line.strip() == '[LID_CONTROLS]':
idxLidControls = index
if line.strip() == '[LID_USAGE]':
idxLidUsage = index
if line.strip() == '[ADJUSTMENTS]':
idxAdjustments = index
llsx.append(line)
index = index + 1
#cambio le righe a sx
v = self.parseRow(lines[idxRainGages + 3], 'RG')
s = '"%s/raingage/%s-%s%sY.txt"' % (inpPath, regime, zona, anni)
s = s.ljust(255, ' ')
v[5] = str(s)
s = "RG_%s" % regime
s = s.ljust(13, ' ')
v[6] = str(s)
llsx[idxRainGages + 3] = "".join(v)
index = idxSubCatchments + 3
v = self.parseRow(lines[index], 'SC')
summAreaLid = dict()
areaPP = areaTV = 0
while v != []:
if v[0][:1] == "S":
#cambio la riga su file sx
s = "%s.0000" % imp
s = s.ljust(13, ' ')
v[4] = str(s)
#genero i dati per LID_USAGE
if convpp:
areaPP = self.getArea(imp, "pp", v[3], convpp)
areaLid = '{:.4f}'.format(areaPP)
lid = [
v[0], "PP".ljust(17, ' '), "1".ljust(13, ' '),
areaLid.ljust(19, ' '), "5.0000".ljust(13, ' '),
"5.0000".ljust(13, ' '), "0.0000".ljust(13, ' '),
"0".ljust(13, ' '), " ".ljust(13, ' ')
]
leadUsage.append(lid)
if convtv:
areaTV = self.getArea(imp, "tv", v[3], convtv)
areaLid = '{:.4f}'.format(areaTV)
lid = [
v[0], "TV".ljust(17, ' '), "1".ljust(13, ' '),
areaLid.ljust(19, ' '), "11.0000".ljust(13, ' '),
"30.0000".ljust(13, ' '), "0.0000".ljust(13, ' '),
"0".ljust(13, ' '), " ".ljust(13, ' ')
]
leadUsage.append(lid)
summAreaLid[v[0]] = areaPP + areaTV
llsx[index] = "".join(v) + "\n"
index = index + 1
v = self.parseRow(lines[index], 'SC')
try:
fsx = open(sxInpFile, 'w')
fsx.writelines(llsx)
fsx.close()
except IOError as err:
return self.render(result=dict(success=0,
message='IOError:%s %s' %
(err.args[-1], sxInpFile)))
#SIMULAZIONE FILE DI SX
try:
st = SWMM5Simulation(str(sxInpFile))
files = st.getFiles()
if isfile(files[1]):
print(files[1])
sxdx = 'sx'
if drwh == 'DX':
sxdx = 'dx'
sxresult = self.parseReport(schema_id, sxdx, files[1],
sxInpFile)
except Exception as err:
return self.render(result=dict(
success=0, message='SWMM5Error:%s %s' % (err, sxInpFile)))
#SE DRWH ESCO
if drwh == 'DX':
print 'RITORNO SXRESULT'
return sxresult
elif drwh == 'SI':
return self.render(result=dict(success=1,
schema=schema_id,
x=xCenter,
y=yCenter,
resultid=self.result_id,
sxresult=sxresult,
dxresult=dxresult))
#ALTRIMENTI SE NON PASSO NE PP NE TV ESCO
elif not convpp + convtv:
return self.render(result=dict(success=0, message='MANCA PP O TV'))
print 'FILE DI DX'
#genero le righe di dx partendo dalle modifiche a sx
#copio il file di sx fino a LID_CONTROLS
for i in range(0, idxLidControls + 3):
lldx.append(llsx[i])
#cambio le righe subcatchment
index = idxSubCatchments + 3
v = self.parseRow(lines[index], 'SC')
while v != []:
if v[0][:1] == "S":
#cambio la riga su file dx
area = float(v[3]) * pow(10, 4)
areaimp = (area * float(imp)) / 100
impdx = int(((areaimp - summAreaLid[v[0]]) /
(area - summAreaLid[v[0]])) * 100)
s = "%s.0000" % impdx
s = s.ljust(13, ' ')
v[4] = str(s)
lldx[index] = "".join(v) + "\n"
index = index + 1
v = self.parseRow(lines[index], 'SC')
#aggiungo la sezione costante dei lid_controls
#import pdb;pdb.set_trace()
flead = open(inpPath + '/lead_controls.inp', 'r')
lldx = lldx + flead.readlines()
flead.close()
#aggiungo LID_USAGE nuovo calcolato
for row in leadUsage:
lldx.append("".join(row) + "\n")
#copio le ultime righe da 2 righe prima di adjusteents alla fine
for i in range(idxAdjustments - 2, len(lines) - 1):
lldx.append(lines[i])
try:
fdx = open(dxInpFile, 'w')
fdx.writelines(lldx)
fdx.close()
except IOError as err:
return self.render(result=dict(success=0,
message='IOError:%s %s' %
(err.args[-1], dxInpFile)))
try:
st = SWMM5Simulation(str(dxInpFile))
files = st.getFiles()
if isfile(files[1]):
print(files[1])
dxresult = self.parseReport(schema_id, 'dx', files[1],
dxInpFile)
except Exception as err:
return self.render(result=dict(
success=0, message='SWMM5Error:%s %s' % (err, dxInpFile)))
return self.render(result=dict(success=1,
schema=schema_id,
x=xCenter,
y=yCenter,
resultid=self.result_id,
sxresult=sxresult,
dxresult=dxresult))
def runSimulation(structure, index, resultType, inp_file_path, out_file_path):
'''
description: run a swmm5 simulation
:param structure: <[string] | string> type of structure,such as 'SUBCATCH', 'NODE', 'LINK', 'SYS'
:param index: <[string] | string> index of specified structure of which the result will be written into result.csv
:param resultType <[int] | int> type of the result data
NOTE: the following document from https://pypi.org/project/SWMM5/
for 'SUBCATCH':
0 Rainfall (in/hr or mm/hr)
1 Snow depth (in or mm)
2 Evaporation loss (in/hr or mm/hr)
3 Infiltration loss (in/hr or mm/hr)
4 Runoff rate (flow units)
5 Groundwater outflow rate (flow units)
6 Groundwater water table elevation (ft or m)
7 Soil Moisture (volumetric fraction, less or equal tosoil porosity)
8 Runoff concentration of TSS (mg/l)
for 'NODE':
0 Depth of water above invert (ft or m)
1 Hydraulic head (ft or m)
2 Volume of stored + ponded water (ft3 or m3)
3 Lateral inflow (flow units)
4 Total inflow (lateral + upstream) (flow units)
5 Flow lost to flooding (flow units)
6 Concentration of TSS (mg/l)
for 'LINK':
0 Flow rate (flow units)
1 Flow depth (ft or m)
2 Flow velocity (ft/s or m/s)
3 Froude number
4 Capacity (fraction of conduit filled)
5 Concentration of TSS (mg/l)
for 'SYS':
0 Air temperature (deg. F or deg. C)
1 Rainfall (in/hr or mm/hr)
2 Snow depth (in or mm)
3 Evaporation + infiltration loss rate (in/hr or mm/hr)
4 Runoff flow (flow units)
5 Dry weather inflow (flow units)
6 Groundwater inflow (flow units)
7 RDII inflow (flow units)
8 User supplied direct inflow (flow units)
9 Total lateral inflow (sum of variables 4 to 8) (flow units)
10 Flow lost to flooding (flow units)
11 Flow leaving through outfalls (flow units)
12 Volume of stored water (ft3 or m3)
13 Evaporation rate (in/day or mm/day)
:param inp_file_path: <string> absolute path of inp file
:param out_file_path: <string> absolute path of output file
the output file will be like:
INDEX VALUE TIME DATE TYPE
1 1.324 00:30 01/01/2020 LEVEL(m)
2 1.484 01:00 02/01/2020 LEVEL(m)
...
i 0.482 00:30 01/01/2020 FLOW(LPS)
...
:return: <generator object SWMM5Simulation.Results at 0x00000207600ED660>
:output: generate the result.csv to save the result if the output path specified
'''
# preprocess the params
structure, index, resultType
if not isinstance(structure, list):
structure = [structure]
if not isinstance(index, list):
index = [index]
if not isinstance(resultType, list):
resultType = [resultType]
# run the simulation
st = SWMM5Simulation(inp_file_path)
# validate the params
if len(structure) != len(index) or len(structure) != len(resultType):
raise Exception('length of structure and index don\'t match')
return st
# if need to generate result.csv
if out_file_path:
if not os.path.exists(os.path.split(out_file_path)[0]):
os.makedirs(os.path.split(out_file_path)[0])
# some setting infos
infoDict = configInp(inp_file_path,
'',
options=[
'REPORT_START_DATE', 'REPORT_START_TIME',
'END_DATE', 'END_TIME'
],
values=[])
step = st.SWMM_ReportStep # the report time step of the unit:min
reportStartDate = infoDict['REPORT_START_DATE']
reportStartTime = infoDict['REPORT_START_TIME']
endDate = infoDict['END_DATE']
endTime = infoDict['END_TIME']
reportStartTimeStamp = time.mktime(
time.strptime(reportStartDate + ' ' + reportStartTime,
'%m/%d/%Y %H:%M:%S'))
endTimeStamp = time.mktime(
time.strptime(endDate + ' ' + endTime, '%m/%d/%Y %H:%M:%S'))
# dict that save the result
result = {'INDEX': [], 'VALUE': [], 'TIME': [], 'DATE': [], 'TYPE': []}
# for every structure
for i in range(len(structure)):
if structure[i] == 'NODE':
vals = list(st.Results('NODE', str(index[i]), resultType[i]))
result['VALUE'] += vals
curTimeStamp = 0
for j in range(len(vals)):
curTimeStamp = reportStartTimeStamp + 30 * 60 * 1000 * i
result['INDEX'].append(index[i])
result['TIME'].append(
time.strftime('%H:%M:%S',
time.localtime(curTimeStamp)))
result['DATE'].append(
time.strftime('%m/%d/%Y',
time.localtime(curTimeStamp)))
result['TYPE'].append(NODE_RESULT_TYPE[resultType[i]])
elif structure[i] == 'LINK':
vals = list(st.Results('LINK', str(index[i]), resultType[i]))
result['VALUE'] += vals
curTimeStamp = 0
for j in range(len(vals)):
curTimeStamp = reportStartTimeStamp + 30 * 60 * 1000 * i
result['INDEX'].append(index[i])
result['TIME'].append(
time.strftime('%H:%M:%S',
time.localtime(curTimeStamp)))
result['DATE'].append(
time.strftime('%m/%d/%Y',
time.localtime(curTimeStamp)))
result['TYPE'].append(LINK_RESULT_TYPE[resultType[i]])
elif structure[i] == 'SUBCATCH':
# TODOS
pass
elif structure[i] == 'SYS':
# TODOS
pass
else:
# REMAIN
pass
df = pd.DataFrame(result)
# index = 0 means no index column
df.to_csv(out_file_path, index=0)
# finally, return the st object
return st
def test_GetSwmmResult_for_runoff_should_return_correct_runoff(self):
SWMM5Simulation("swmm5/examples/simple/swmm5Example.inp")
def test_SWMM5Simulation_should_have_attributes(self):
st=SWMM5Simulation("swmm5/examples/simple/swmm5Example.inp")
st.SWMM_Nperiods
def swmm_plot():
output_file("layout.html")
spreadsheet_name = 'data/dados-calib.xlsx'
swmm_filename = 'results/partial.inp'
st = SWMM5Simulation(swmm_filename) # run a simulation
wb = openpyxl.load_workbook(spreadsheet_name)
abas = wb.sheetnames
observado_todos = np.array([], dtype=np.float)
simulado_todos = np.array([], dtype=np.float)
for aba in abas:
sheet = wb[aba]
sheet.cell(row=1, column=4).value = 'Nivel_simulado'
# lendo o nivel medido
nivel_medido = np.array([], dtype=np.float)
for i in range(2, sheet.max_row + 1):
nivel_medido = np.append(nivel_medido,
sheet.cell(row=i, column=3).value)
# pegando o simulado do swmm
nivel_simulado = np.array(list(st.Results('NODE', aba, 0)))
# escrevendo o simulado no excel
ndados = st.SWMM_Nperiods
for i in range(2, np.size(nivel_simulado) + 2):
sheet.cell(row=i, column=4).value = nivel_simulado[i - 2]
observado_todos = np.concatenate((observado_todos, nivel_medido))
simulado_todos = np.concatenate((simulado_todos, nivel_simulado))
data = np.array([], dtype=np.float)
hora = np.array([], dtype=np.float)
datahora = np.array([], dtype=np.datetime64)
for i in range(2, sheet.max_row + 1):
data = np.append(data, sheet.cell(row=i, column=1).value)
hora = np.append(hora, sheet.cell(row=i, column=2).value)
#print(sheet.cell(row=i,column=2).value)
diazin = sheet.cell(row=i, column=1).value
horinha = sheet.cell(row=i, column=2).value
datahora = np.append(
datahora, diazin + timedelta(seconds=60 * 60 * horinha.hour +
60 * horinha.minute + horinha.second))
observado_todos = np.array(observado_todos, dtype=np.float)
simulado_todos = simulado_todos * 100
wb.save(spreadsheet_name)
num = np.nansum((observado_todos - simulado_todos)**2)
den = np.nansum((observado_todos - np.nanmean(observado_todos))**2)
numlog = np.nansum((np.log(observado_todos) - np.log(simulado_todos))**2)
denlog = np.nansum(
(np.log(observado_todos) - np.log(np.nanmean(observado_todos)))**2)
if den == 0:
den = 0.0000001
if denlog == 0:
denlog = 0.0000001
print('Nash: %.4f' % (1 - num / den))
print('Log-Nash: %4f' % (1 - numlog / denlog))
observado_todos = observado_todos.reshape(
int(len(observado_todos) / ndados), ndados)
simulado_todos = simulado_todos.reshape(int(len(simulado_todos) / ndados),
ndados)
simulado_todos = simulado_todos / 100
x = datahora
yobs = observado_todos
ysim = simulado_todos
nash = {}
nashlog = {}
for i, aba in enumerate(abas):
num = np.nansum((yobs[i] - ysim[i])**2)
den = np.nansum((yobs[i] - np.nanmean(yobs[i]))**2)
numlog = np.nansum((np.log(yobs[i]) - np.log(ysim[i]))**2)
denlog = np.nansum((np.log(yobs[i]) - np.log(np.nanmean(yobs[i])))**2)
if den == 0:
den = 0.0000001
if denlog == 0:
denlog = 0.0000001
nash[aba] = 1 - num / den
nashlog[aba] = 1 - numlog / denlog
figuras = {}
headers = {}
for i, aba in enumerate(abas):
headers[aba] = 'Nash: %.4f' % nash[
aba] + ' | ' + 'Log-Nash: %.4f' % nashlog[aba]
figuras[aba] = figure(width=2100,
plot_height=500,
title=aba + ' | ' + headers[aba],
x_axis_type="datetime")
figuras[aba].title.text_font_size = '16pt'
figuras[aba].xaxis.axis_label_text_font_size = '14pt'
figuras[aba].yaxis.axis_label_text_font_size = '14pt'
figuras[aba].circle(x, yobs[i], fill_color=None, line_color="navy")
figuras[aba].line(x, yobs[i], color='navy')
figuras[aba].line(x, ysim[i], color='firebrick')
figs = [[figuras[figura]] for figura in abas]
grid = gridplot(figs)
show(grid)
def test_running_with_good_input_file(self):
"""should not raise exceptoins"""
SWMM5Simulation("swmm5/examples/simple/swmm5Example.inp")
def run_swmm(self):
self.st = SWMM5Simulation(self.swmm_filename) # run a simulation
import numpy as np
import matplotlib # we need matplotlib for plotting later
matplotlib.use('Qt5Agg') # Not esential. But QtAgg backend seems to work better for plotting
import matplotlib.pyplot as plt # for plotting later
from swmm5.swmm5tools import SWMM5Simulation
from string import Template
area2=75
area3=90
maxfs=[]
for area1 in range(0,100,5):
with open("small_case.template", 'r') as inf:
with open("trial.inp", 'w') as outf:
ins=inf.read()
ins=Template(ins)
outs=ins.substitute(a1=area1, a2=area2, a3=area3)
outf.write(outs)
#now run
st=SWMM5Simulation("trial.inp")
maxf=max(st.Results('NODE','N1', 4))
print (maxf)
maxfs.append(maxf)
ggplot( aesthetics= aes(x = 'weight', y = 'hindfoot_length'), data = surveys_complete)
plt.plot()
import matplotlib # we need matplotlib for plotting later
matplotlib.use(
'Qt5Agg'
) # Not esential. But QtAgg backend seems to work better for plotting
import matplotlib.pyplot as plt # for plotting later
from swmm5.swmm5tools import SWMM5Simulation
st = SWMM5Simulation("small_case.inp")
print("Version = ", st.SWMM5_Version())
print("Number of nodes = ", st.SWMM_Nnodes)
# get the flow in conduit LO3
flow = st.Results(
"LINK", "LO3", 0
) # 0 is the code for flow in a link (see docs: https://pypi.org/project/SWMM5/
flow = list(flow) # convert the results to a list.
# calculate time list
time = [st.SWMM_ReportStep * x / 3600. for x in range(st.SWMM_Nperiods)]
ax = plt.plot(time, flow, 'r', marker="^") # plot press against daias
plt.xlabel("Time (h)") # set axes labels
plt.ylabel("Flow in LO3 (m$^3$/s)")
plt.show() # show the plot.
def simulazione(self,rete='CS',schema='H',imp='15',regime='CAM',anni='2',convpp='',convtv='',callback='',_=''):
#apro il file di base e sostituisco i valori
filePath = "/apps/trigeau/data"
fileName = "%s/%s_%s.inp" %(filePath,rete,schema)
fileName = "%s/%s_%s.inp" %(filePath,rete,schema)
zona="Chicago"
rsRandom = randomString()
sxInpFile = "/tmp/%s_%s_%s_sx.inp" %(rete,schema,rsRandom)
dxInpFile = "/tmp/%s_%s_%s_dx.inp" %(rete,schema,rsRandom)
sxInpFile = "/tmp/%s_%s_sx.inp" %(rete,schema)
dxInpFile = "/tmp/%s_%s_dx.inp" %(rete,schema)
llsx=[]
lldx=[]
leadUsage=[]#array co i leadusage calcolati
html=''
fileInp = open(fileName, 'r')
lines = fileInp.readlines()
index=0
for line in lines:
if line.strip()=='[RAINGAGES]':
idxRainGages = index
if line.strip()=='[SUBCATCHMENTS]':
idxSubCatchments = index
if line.strip()=='[LID_USAGE]':
idxLidUsage = index
if line.strip()=='[ADJUSTMENTS]':
idxAdjustments = index
llsx.append(line)
index=index+1
#cambio le righe a sx
v=self.parseRow(lines[idxRainGages+3],'RG')
s='"%s/%s-%s%sY.txt"'%(filePath,regime,zona,anni)
s=s.ljust(255,' ')
v[5]=str(s)
s="%s-RG"%regime
s=s.ljust(13,' ')
v[6]=str(s)
llsx[idxRainGages+3]="".join(v)
index = idxSubCatchments+3
v=self.parseRow(lines[index],'SC')
summArea=0
while v!=[]:
if v[0][:1] == "S":
#cambio la riga su file sx
s="%s.0000"%imp
s=s.ljust(13,' ')
v[4]=str(s)
#genero i dati per LID_USAGE
if convpp:
area=self.calcoloArea(imp,"pp",v[3])*float(convpp)
summArea=summArea+area
area='{:.4f}'.format(area)
lid=[
v[0],
"PP".ljust(17,' '),
"2".ljust(13,' '),
area.ljust(19,' '),
"5.0000".ljust(13,' '),
"10.0000".ljust(13,' '),
"0.0000".ljust(13,' '),
"0".ljust(13,' '),
" ".ljust(13,' ')
]
leadUsage.append(lid)
if convtv:
area=self.calcoloArea(imp,"tv",v[3])*float(convtv)
summArea=summArea+area
area='{:.4f}'.format(area)
lid=[
v[0],
"TV".ljust(17,' '),
"1".ljust(13,' '),
area.ljust(19,' '),
"11.0000".ljust(13,' '),
"30.0000".ljust(13,' '),
"0.0000".ljust(13,' '),
"0".ljust(13,' '),
" ".ljust(13,' ')
]
leadUsage.append(lid)
llsx[index]="".join(v)+"\n"
index=index+1
v=self.parseRow(lines[index],'SC')
fsx = open(sxInpFile, 'w')
fsx.writelines(llsx)
fsx.close()
#FILE DI DX
#genero le righe di dx partendo dalle modifiche a sx
#copio iol file di sx fino a LID_USAGE
for i in range(0,idxLidUsage+3):
lldx.append(llsx[i])
#cambio le righe subcatchment
index = idxSubCatchments+3
v=self.parseRow(lines[index],'SC')
while v!=[]:
if v[0][:1] == "S":
#cambio la riga su file dx
area=float(v[3])*pow(10,4)
impdx=66#(area*imp-summArea)/(area-summArea)
s="%s.0000"%impdx
s=s.ljust(13,' ')
v[4]=str(s)
lldx[index]="".join(v)+"\n"
index=index+1
v=self.parseRow(lines[index],'SC')
#aggiungo LID_USAGE nuovo calcolato
for row in leadUsage:
lldx.append("".join(row)+"\n")
#copio le ultime righe da 2 righe prima di adjusteents alla fine
for i in range(idxAdjustments-2,len(lines)-1):
lldx.append(lines[i])
fdx = open(dxInpFile, 'w')
fdx.writelines(lldx)
fdx.close()
result=dict(success=0)
st=SWMM5Simulation(str(sxInpFile))
files=st.getFiles()
if isfile(files[1]):
print files[1]
result["sx"]=self.parseReport(files[1])
st=SWMM5Simulation(str(dxInpFile))
files=st.getFiles()
if isfile(files[1]):
print files[1]
result["dx"]=self.parseReport(files[1])
RDII = 84 # LPS
if __name__ == "__main__":
# 0. set a mock example-case.inp (DONE)
# setFlowOnAverage([['37', 48, '03:00','10:30'],['53', 36, '03:00','10:30']],
# out_file_path = os.path.join(config['publicPath'], 'mock', 'observed', 'example-case.inp'))
# 1. get the try-case result (DONE)
# exampleCaseInpPath = os.path.join(config['publicPath'], 'mock', 'observed', 'example-case.inp')
# exampleCaseOutPath = os.path.join(config['publicPath'], 'mock', 'observed', 'result.csv')
# runSimulation(['NODE'] * 4, ['13', '36', '57', '64'], [0, 0, 0, 4], exampleCaseInpPath, exampleCaseOutPath)
# 2. start GLUE
inpFilePath = os.path.join(config['publicPath'], 'mock', 'random',
'try-case.inp')
outFilePath = os.path.join(config['publicPath'], 'mock', 'random',
'result.csv')
st = SWMM5Simulation(
os.path.join(config['publicPath'], 'static', 'baseline.inp'))
links = st.Link()
cnt = 0
startTime = ['03:00'] * len(links)
endTime = ['10:30'] * len(links)
# log Nash-Sutcliffe efficiency coefficient of every iteration
NS_log = []
# log scenario coefficient of every iteration
scenario_log = []
while cnt < ITERATION_TIMES:
# 2.1 parameter sampling (prior distribution: uniform distribution)
scenario = splitRDIIRandomlyIntoPipes(RDII=RDII,
pipes=links,
startTime=startTime,
from swmm5.swmm5tools import SWMM5Simulation
st = SWMM5Simulation("small_case.INP")
print("")
print(st.SWMM5_Version())
print(st.Subcatch())
from swmm5.swmm5tools import SWMM5Simulation
st = SWMM5Simulation("swmm5/examples/simple/swmm5Example.inp")
import xlrd
from bokeh.io import output_file, show
from bokeh.layouts import gridplot
from bokeh.palettes import Viridis3
from bokeh.plotting import figure, ColumnDataSource
from bokeh.models import HoverTool
from datetime import datetime, timedelta
from collections import OrderedDict
import time
output_file("layout.html")
spreadsheet_name = 'output.xlsx'
swmm_filename = 'sao_carlos.inp'
st = SWMM5Simulation(swmm_filename) # run a simulation
wb = openpyxl.load_workbook(spreadsheet_name)
abas = wb.get_sheet_names()
observado_todos = np.array([], dtype=np.float)
simulado_todos = np.array([], dtype=np.float)
for aba in abas:
sheet = wb.get_sheet_by_name(aba)
sheet.cell(row=1, column=4).value = 'simulated_depth'
# lendo o nivel medido
nivel_medido = np.array([], dtype=np.float)
for i in range(2, sheet.max_row + 1):
nivel_medido = np.append(nivel_medido,
def swmmRun(swmminputfile, parameters):
st = SWMM5Simulation(swmminputfile, clean=True)
results = np.array(list(st.Results(*parameters.swmmResultCodes)))
return results
