def test_gettables():
"""py.test for gettables"""
thedata = (
(
[
("Site and Source Energy", [["a", "2"], ["3", "4"]]),
("Site to Source Energy Conversion Factors", [["b", "6"], ["7", "8"]]),
("Custom Monthly Report", [["c", "16"], ["17", "18"]]),
("Custom Monthly Report", [["d", "26"], ["27", "28"]]),
],
False,
), # titlerows, tofloat
(
[
("Site and Source Energy", [["a", 2], [3, 4]]),
("Site to Source Energy Conversion Factors", [["b", 6], [7, 8]]),
("Custom Monthly Report", [["c", 16], [17, 18]]),
("Custom Monthly Report", [["d", 26], [27, 28]]),
],
True,
), # titlerows, tofloat
)
for titlerows, tofloat in thedata:
result = readhtml.titletable(SAMPLE_HTML, tofloat=tofloat)
for (title1, rows1), (title2, rows2) in zip(result, titlerows):
assert title1 == title2
assert rows1 == rows2
assert result == titlerows
def tablebyindex(filehandle, index):
"""fast extraction of the table using the index to identify the table
This function reads only one table from the HTML file. This is in contrast to `results.readhtml.titletable` that will read all the tables into memory and allows you to interactively look thru them. The function `results.readhtml.titletable` can be very slow on large HTML files.
This function is useful when you know which file you are looking for. It does not work with negative indices, like you can in a list. If you know a way to make negative indices work, do a pull request :-)
Parameters
----------
fhandle : file like object
A file handle to the E+ HTML table file
index: int
This is the index of the table you are looking for
Returns
-------
titleandtable : (str, list)
- (title, table)
- title = previous item with a <b> tag
- table = rows -> [[cell1, cell2, ..], [cell1, cell2, ..], ..]
"""
with filehandle:
tableindex = 0
for i in range(index + 1):
thetable = get_upto_nexttable(filehandle)
filehandle = StringIO(thetable)
htables = readhtml.titletable(filehandle)
try:
return htables[0]
except IndexError as e:
None
def test_gettables():
"""py.test for gettables"""
thedata = (
([('Site and Source Energy', [['a', '2'], ['3', '4']]),
('Site to Source Energy Conversion Factors', [['b', '6'], ['7',
'8']]),
('Custom Monthly Report', [['c', '16'], ['17', '18']]),
('Custom Monthly Report', [['d', '26'],
['27',
'28']])], False), # titlerows, tofloat
([('Site and Source Energy', [['a', 2], [3, 4]]),
('Site to Source Energy Conversion Factors', [['b', 6], [7, 8]]),
('Custom Monthly Report', [['c', 16], [17, 18]]),
('Custom Monthly Report', [['d', 26],
[27, 28]])], True), # titlerows, tofloat
)
for titlerows, tofloat in thedata:
# print titlerows
result = readhtml.titletable(SAMPLE_HTML, tofloat=tofloat)
for (title1, rows1), (title2, rows2) in zip(result, titlerows):
# print title1, title2
assert title1 == title2
# print rows1, rows2
assert rows1 == rows2
assert result == titlerows
def __init__(self):
#Change EplusPath Here
eplusPath="D:/EnergyPlusV8-6-0/energyplus.exe"
#Automate Setting
self.path=os.path.abspath('.')
sys.path.append(self.path)
self.idfName = "test6.idf"
self.eplusPath=eplusPath
self.iddFile = "Energy+.idd"
IDF.setiddname(self.iddFile)
self.outputPath="."
self.idf1 = IDF(self.idfName)
self.epwFile = "SGP_Singapore.486980_IWEC.epw"
esoPath="5ZoneAirCooled.eso"
#Call Eplus Here
# subprocess.call([self.eplusPath,'-i',self.iddFile,'-w',self.epwFile,'-d',self.outputPath,self.idfName])
self.htmFile = "eplustbl.htm"
fileHandle = open(self.htmFile, 'r').read() # get a file handle to the html file
self.eplusOutputTables = readhtml.titletable(fileHandle) # reads the tables with their titles
self.ETTV=Class_ETTV.ETTV()
self.ETTV.ETTVCalculator(self.eplusOutputTables)
self.RTTV=Class_RTTV.RTTV()
self.RTTV.RTTVCalculator(self.eplusOutputTables,self.idf1)
self.Trop=Class_Trop.Trop()
self.Trop.TropCalculator(self.eplusOutputTables,self.RTTV.RTTV)
self.TDSE=Class_TDSE.TDSE()
self.TDSE.TDSECalculator(self.eplusOutputTables,esoPath)
#Output
self.get()
def setfolder(self):
"""
set folder and set html file
:return:
"""
files = os.listdir(self.folder)
for file in files:
print(file)
if file.endswith('Table.html'):
html = open((str(self.folder) + '/' + file), 'r').read()
self.htables = readhtml.titletable(html)
name = file[0:-10] + '.csv'
def test_gettables():
"""py.test for gettables"""
thedata = (
(
[
(
'Site and Source Energy',
[['a', '2'], ['3', '4']]
),
(
'Site to Source Energy Conversion Factors',
[['b', '6'], ['7', '8']]
),
(
'Custom Monthly Report',
[['c', '16'], ['17', '18']]
),
(
'Custom Monthly Report',
[['d', '26'], ['27', '28']]
)
], False), # titlerows, tofloat
(
[
(
'Site and Source Energy',
[['a', 2], [3, 4]]
),
(
'Site to Source Energy Conversion Factors',
[['b', 6], [7, 8]]
),
(
'Custom Monthly Report',
[['c', 16], [17, 18]]
),
(
'Custom Monthly Report',
[['d', 26], [27, 28]]
)
], True), # titlerows, tofloat
)
for titlerows, tofloat in thedata:
# print titlerows
result = readhtml.titletable(SAMPLE_HTML, tofloat=tofloat)
for (title1, rows1), (title2, rows2) in zip(result, titlerows):
# print title1, title2
assert title1 == title2
# print rows1, rows2
assert rows1 == rows2
assert result == titlerows
def run_models(self, processors):
if self.idf is None:
raise TypeError('idf not set')
util = EppyUtilIdf()
idf_lst = []
file_dir = os.path.dirname(__file__)
output_folder = os.path.join(file_dir, 'sensitivity_results')
try:
shutil.rmtree(output_folder)
except FileNotFoundError as e:
print(e)
os.mkdir(output_folder)
for i, values in enumerate(self.X):
idf_temp = util.copy(self.idf)
for j, value in enumerate(values):
for obj in self.objects[j]:
obj_id, obj_name, field = obj.split(',')
util.mod(idf_temp, obj_id, obj_name, field, value)
idf_temp.idfname = os.path.join(output_folder,
'run-{}.idf'.format(i))
idf_temp.save()
sim_settings = {
'ep_version': '8-7-0',
'verbose': 'q',
'output_directory': output_folder,
'readvars': True,
'output_prefix': "run-{}-".format(i)
}
idf_lst.append([idf_temp, sim_settings])
run_functions.runIDFs(idf_lst, processors=processors)
# retrieve E+ outputs after simulations are run
num_samples = self.X.shape[0]
self.y = np.zeros(num_samples)
for k in range(num_samples):
result_file = os.path.join(output_folder,
'run-{}-tbl.htm'.format(k))
with open(result_file, "r", encoding="ISO-8859-1") as f:
results_table = readhtml.titletable(f.read())
total_site_energy = util.get_output(
results_table, ['Site and Source Energy', 'Total Site Energy'])
total_site_energy_per_area = total_site_energy[1]
self.y[k] = total_site_energy_per_area
def results():
t_id = []
cooling = []
heating = []
files = glob('sim_result/*.htm')
i = 1
for file1 in files:
filehandle = open(file1, 'r').read()
htables = readhtml.titletable(filehandle)
t_id.append(
re.search('sim_result/([a-z]*)([0-9]*).idf(.*).htm',
file1).group(2))
cooling.append(htables[3][1][-1][4])
heating.append(htables[3][1][-1][5])
print(str(i) + "/" + str(total))
i = i + 1
data_raw = {'id': t_id, 'cooling': cooling, 'heating': heating}
df = pd.DataFrame(data_raw)
df.to_csv("model_outputs.csv", index=False)
def getfromhtm(fhandle, htm_options):
"""getdata from htm"""
returndict = dict(htm_options)
if htm_options.setdefault("as_tables", None):
htables = readhtml.titletable(fhandle)
returndict["result"] = [list(table) for table in htables]
# made into a list for json conversion
# json conversion makes it a list
# so now testing is easier
elif isinstance(htm_options.setdefault("tableindex", None), int):
header, table = fasthtml.tablebyindex(fhandle, htm_options["tableindex"])
elif htm_options.setdefault("tablename", None):
header, table = fasthtml.tablebyname(fhandle, htm_options["tablename"])
if htm_options.setdefault("table"):
returndict["result"] = [header, table]
elif htm_options.setdefault("rows"):
irows = htm_options["rows"]
parttable = list()
for i in irows:
parttable.append(table[i])
returndict["result"] = [header, parttable]
elif htm_options.setdefault("cols"):
icols = htm_options["cols"]
parttable = list()
for row in table:
partrow = list()
for i in icols:
partrow.append(row[i])
parttable.append(partrow)
returndict["result"] = [header, parttable]
elif htm_options.setdefault("cells"):
cells = htm_options["cells"]
somecells = list()
for cell in cells:
somecells.append(table[cell[0]][cell[1]])
returndict["result"] = [header, somecells]
return returndict
def tablebyname(filehandle, header):
"""fast extraction of the table using the header to identify the table
This function reads only one table from the HTML file. This is in contrast to `results.readhtml.titletable` that will read all the tables into memory and allows you to interactively look thru them. The function `results.readhtml.titletable` can be very slow on large HTML files.
This function is useful when you know which file you are looking for. It looks for the title line that is in bold just before the table. Some tables don't have such a title in bold. This function will not work for tables that don't have a title in bold
Parameters
----------
fhandle : file like object
A file handle to the E+ HTML table file
header: str
This is the title of the table you are looking for
Returns
-------
titleandtable : (str, list)
- (title, table)
- title = previous item with a <b> tag
- table = rows -> [[cell1, cell2, ..], [cell1, cell2, ..], ..]
"""
htmlheader = f"<b>{header}</b><br><br>"
with filehandle:
for line in filehandle:
line = _decodeline(line)
if line.strip() == htmlheader:
justtable = getnexttable(filehandle)
thetable = f"{htmlheader}\n{justtable}"
break
filehandle = StringIO(thetable)
htables = readhtml.titletable(filehandle)
try:
return list(htables[0])
except IndexError as e:
None
idf_run.run(output_directory=saida,expandobjects=True)
for idf in lista_idfs_AC:
nome = idf.replace('.idf','')
saida = os.getcwd() + "\%s" % nome
idf_run = IDF(idf, epw)
idf_run.run(output_directory=saida,expandobjects=True)
'''
# RESULTADOS AC
for i in range(len(lista_idfs_AC)):
fname = "C%d AC/eplustbl.htm"%i
filehandle = open(fname,'r').read()
htables = readhtml.titletable(filehandle)
firstitem = htables[0]
firstitem_table = firstitem[1]
secondrow = firstitem_table[2]
consumo_total = secondrow[1]
result_AC.write("C%d AC " %i + str(consumo_total) + '\n')
# RESULTADOS VN
for i in range(len(lista_idfs_VN)):
sum_serie = 0
path = "C%d VN/eplusout.eso"%i
dd, data = esoreader.read(path)
frequency, key, variable = dd.find_variable('Zone Thermal Comfort ASHRAE 55 Adaptive Model 80% Acceptability Status')[0]
idx = dd.index[frequency, key, variable]
time_series = data[idx]
num_hours =8760
import sys
import os
from eppy.results import readhtml
lista_de_valores = []
archivoIDF = sys.argv[1]
carpeta = os.path.join(os.path.dirname(archivoIDF),
"optimizacion_" + os.path.splitext(os.path.basename(archivoIDF))[0])
fname = os.path.join(carpeta, "eplustbl.htm") # archivo html valor
filehandle = open(fname, 'r').read() # get a file handle to the html file
htables = readhtml.titletable(filehandle) # reads the tables with their titles
valores = []
valores.append(htables[0][1][1][1])
valores.append(htables[0][1][3][1])
valores.append(htables[17][1][2][5])
valores.append(htables[17][1][3][4])
valores.append(htables[11][1][1][1])
valores.append(htables[6][1][2][5])
lista_de_valores.append(valores)
header = []
# TITLE Total Energy (kWh) / Total Site Energy
attrib = ('[' + str(htables[0][1][0][1]) +
'/' + str(htables[0][1][1][0]) + ']')
header.append(attrib)
# TITLE Total Energy (kWh) / Total Source Energy
attrib1 = ('[' + str(htables[0][1][0][1]) +
'/' + str(htables[0][1][3][0]) + ']')
header.append(attrib1)
