def __init__(self, ax, fig, user, par=None):
if par is None:
par = ['0']
self.fig = fig
self.ax = ax
self.ful = rcsv('{}\{}.ful'.format(*user[2:4]), sep=',')
self.parameters = par
def asignar_sun(dataframe,
CVE_MUN='CVE_MUN',
vars=['CVE_MUN', 'CVE_SUN', 'NOM_SUN', 'TIPO_SUN']):
sun = rcsv(
r'D:\PCCS\01_Analysis\01_DataAnalysis\00_Parametros\sun_main.csv',
dtype={
'CVE_SUN': str,
'CVE_ENT': str,
'CVE_MUN': str,
'CVE_LOC': str
},
encoding='UTF-8',
)
sun['CVE_SUN'] = sun['CVE_SUN'].apply('{:0>3}'.format)
sun['CVE_ENT'] = sun['CVE_ENT'].apply('{:0>2}'.format)
sun['CVE_MUN'] = sun['CVE_MUN'].apply('{:0>5}'.format)
print('Catalogo de variables. Default vars = {}'.format(vars))
print(list(sun))
if 'CVE_MUN' not in vars: vars.append('CVE_MUN')
dataframe.rename(
columns={CVE_MUN: 'CVE_MUN'}, inplace=True
) # Estandariza el nombre de la columna de clave geoestadistica
sun = sun[vars]
dataframe = pd.merge(dataframe, sun, on='CVE_MUN')
return dataframe
def alfa_t2(self, name, property=None):
ffile = rcsv('{}.ful'.format(name), sep=',')
fire_site = self.mc_rand(ffile)
config = ffile.iloc[fire_site]
fuel_xes = (config.XA, config.XB)
fuel_yes = (config.YA, config.YB)
fuel_zes = (config.ZA, config.ZB)
hrrpua = triangular(config.hrrpua_min, config.hrrpua_max, mode=config.hrrpua_mode) * 1000 # kW/m2
if not property:
alpha = triangular(config.alpha_min, config.alpha_max, mode=config.alpha_mode) # kW/s2
elif property == 'store':
alpha = hrrpua * random.lognormal(-9.72, 0.97) # kW/s2
area = min(config.hrr_max / hrrpua * 1000, self.a_max) # m2
area = config.hrr_max / hrrpua * 1000 # m2
print('alpha:{}, hrrpua:{}'.format(alpha, hrrpua))
hrr = []
for t_frag in range(0, 120):
t = self.t_end * t_frag/119
hrr.extend([round(i, 4) for i in [t/60, alpha / 1000 * (t ** 2)]])
if hrr[-1] > area * hrrpua:
hrr[-1] = area * hrrpua
return hrr, area, fuel_zes, fuel_xes, fuel_yes, hrrpua, alpha
def sprink_noeff(self, name, property=None):
ffile = rcsv('{}.ful'.format(name), sep=',')
fire_site, ases = self.mc_rand(ffile)
config = ffile.iloc[fire_site]
fuel_xes = (config.XA, config.XB)
fuel_yes = (config.YA, config.YB)
fuel_zes = (config.ZA, config.ZB)
hrrpua = triangular(config.hrrpua_min, config.hrrpua_max, mode=config.hrrpua_mode) * 1000 # [kW]
if not property:
alpha = triangular(config.alpha_min, config.alpha_max, mode=config.alpha_mode) # [kW/s2]
elif property == 'store':
alpha = hrrpua * random.lognormal(-9.72, 0.97) # [kW/s2]
# q_0 = min(alpha * config.t_sprink ** 2, self.a_max * hrrpua) # [kW]
q_0 = alpha * config.t_sprink ** 2 # [kW]
area = q_0 / hrrpua # [m2]
print('alpha:{}, hrrpua:{}'.format(alpha, hrrpua))
hrr = []
for t_frag in range(0, 120):
t = self.t_end * t_frag/120
if t >= config.t_sprink:
# [min], [kW/s2 * 1k * s2]=[MW]
hrr.extend([round(i, 4) for i in [t/60, alpha / 1000 * (config.t_sprink ** 2)]])
else:
hrr.extend([round(i, 4) for i in [t/60, alpha / 1000 * (t ** 2)]])
return hrr, area, fuel_zes, fuel_xes, fuel_yes, hrrpua, alpha
def asignar_sun(dataframe,
CVE_MUN='CVE_MUN',
variables=['CVE_MUN', 'CVE_SUN', 'NOM_SUN', 'TIPO_SUN']):
# Cargar archivo del Subsistema Principal del SUN
sun = rcsv(
r'D:\PCCS\01_Dmine\00_Generales\sun_main.csv',
dtype={
'CVE_SUN': str,
'CVE_ENT': str,
'CVE_MUN': str,
'CVE_LOC': str,
'CVE_SUNMUN': str
},
encoding='UTF-8',
)
sun['CVE_SUN'] = sun['CVE_SUN'].apply('{:0>3}'.format)
sun['CVE_ENT'] = sun['CVE_ENT'].apply('{:0>2}'.format)
sun['CVE_MUN'] = sun['CVE_MUN'].apply('{:0>5}'.format)
# print('Catalogo de variables. Default vars = {}'.format(vars))
# print(list(sun))
if 'CVE_MUN' not in variables: variables.append('CVE_MUN')
dataframe.rename(
columns={CVE_MUN: 'CVE_MUN'}, inplace=True
) # Estandariza el nombre de la columna de clave geoestadistica
sun.drop_duplicates(
'CVE_SUNMUN', keep='first', inplace=True
) # Quita los municipios que en el dataset aparecen duplicados por estar subdivididos en localidades
sun = sun[variables]
dataframe = pd.merge(dataframe, sun, on='CVE_MUN')
# Eliminar registros que no formen parte del Subsistema Principal
return dataframe
def __init__(self, res_path, t_crit, rset, probs):
chdir(res_path)
self.results = rcsv('stoch_rest.csv', sep=',')
self.t_crit = t_crit
self.rset = rset
self.p_coll = probs[0]
self.p_evac = probs[1]
def mean(self):
data_list = []
for t in range(self.time[0], self.time[1], 10):
for o in self.orientation:
# split csv files for specified bounds
with open('f2a_{}_{}.csv'.format(t, o)) as temp:
files = temp.read().split('Patch ')
[self.split_csv(f) for f in files[1:]]
# merge splitted csv files using Y as key
data_list.append(rcsv('f2a_{}_{}.csv'.format(t, o), sep=','))
break
break
def generate_sim(data_path):
t = sec()
chdir(config['results_path'])
data_set = rcsv(data_path)
for i, r in data_set.iterrows():
if r['profile'] != r['profile']:
with open('{0}\{0}.err'.format(r['ID']), 'w') as err:
mess = '[WARNING] There are no elements above the fire base in scenario {}'.format(r['ID'])
err.write('{}\nMax element temperature in te scenario is equal to the ambient temperature'.format(mess))
print(out(outpth, mess))
continue
chdir(str(r['ID']))
MultiT2D(config['time_end']).prepare(r)
chdir('..')
return '[OK] {} simulation files created ({} s)'.format(len(data_set.index), round(sec() - t, 2))
def __init__(self, t_end, title, fire_type, fuelconfig):
self.t_end = t_end # simulation duration time
self.title = title # simulation title
self.f_type = fire_type # type of fire
self.fire_coords = [] # to export to Single class
print(out(outpth, 'Reading fuel configuration files...'), end='\r')
t = sec()
if fuelconfig == 'stp':
self.fuel = fires.Fuel(title).read_fuel() # import fuel from STEP and FUL config files
elif fuelconfig == 'obj':
self.fuel = fires.FuelOBJ(title).read_fuel() # import fuel from OBJ and FUL config files
else:
self.fuel = rcsv('{}.ful'.format(title))
print(out(outpth, '[OK] Fuel configuration imported ({} s)'.format(round(sec() - t, 2))))
def save(self, rset, t_crit, errors):
rset = int(rset)
t_crit = int(t_crit)
data = rcsv('stoch_rest.csv', sep=',')
num_nocoll = len(data.time_crit[data.time_crit == 0])
n_iter = len(data.t_max)
save_list = ['v{}\n\nResults from {} iterations\n'.format(self.ver, n_iter)]
err = [1, 1] # actual uncertainty of calculation
# calculating and writing exceeding critical temperature probability and uncertainty to the list
try:
p_coll = len(data.t_max[data.t_max < int(t_crit)]) / len(data.t_max)
save_list.append('P(collapse) = {}\n'.format(1 - p_coll))
except ZeroDivisionError:
save_list.append('unable to calculate P(ASET<RSET) and RMSE\n')
p_coll = 0
err[0], save_list = self.uncertainity(save_list, p_coll, n_iter)
# calculating and writing ASET<RSET probability and uncertainty to the list
try:
p_evac = (len(data.time_crit[data.time_crit <= int(rset)]) - num_nocoll) / (
len(data.time_crit) - num_nocoll)
save_list.append('P(ASET < RSET) = {}\n'.format(p_evac))
except ZeroDivisionError:
save_list.append('unable to calculate P(ASET<RSET) and RMSE\n')
p_evac = 0
err[1], save_list = self.uncertainity(save_list, p_evac, n_iter)
save_list.append('{} OZone errors occured'.format(errors))
with open('results.txt', 'w') as file:
file.writelines(save_list)
# draw charts
print('temp_crit={}\nRSET={}'.format(t_crit, rset))
Charting(self.r_p, t_crit, rset, (p_coll, p_evac)). draw()
# check if uncertainty is low enough to stop calculations
if 0 < err[0] < 0.001 and 0 < err[1] < 0.001:
return True
else:
return False
def newzealand1(self, name):
fuel_height = (0.5, 18.5)
fuel_xes = (0.5, 9.5)
fuel_yes = (0.5, 19.5)
hrr_max = 50
config = rcsv('{}.ful'.format(name), sep=',')
print(float(config.alpha_mode))
alpha = triangular(*config.alpha_min, *config.alpha_max, mode=float(config.alpha_mode))
hrrpua = triangular(*config.hrrpua_min, *config.hrrpua_max, mode=float(config.hrrpua_mode))
area = hrr_max / hrrpua
print('alpha:{}, hrrpua:{}'.format(round(alpha, 4), round(hrrpua,4)))
hrr = []
for i in range(0, int(self.t_end/120)):
hrr.extend([i / 60, round(alpha / 1000 * (i ** 2), 4)])
if hrr[-1] > hrr_max:
hrr[-1] = hrr_max
return hrr, area, fuel_height, fuel_xes, fuel_yes
def _read_csv(cls, input_file):
df_data = rcsv(input_file)
df_data['学号'] = df_data['学号'].astype('str')
df_data.set_index('学号')
dicts = []
for index, row in df_data.iterrows():
if not row['实验结论为空']:
sentence = {
'id' : row['学号'],
'isTrain' : row['isTrain'],
'content' : row['content'],
'a' : row['结论跑题'],
'b' : row['提到变频器多频率，改变频率多个值之类的'],
'c' : row['变频器一定，流量值稳定'],
'd' : row['频率越大，流量值越大'],
'e' : row['流量计能反映瞬时流量值，数据准确'],
}
dicts.append(sentence)
return dicts
def newzealand2(self, name):
fuel_height = (0.32, 34.1)
fuel_xes = (0.3, 23.1)
fuel_yes = (10.3, 101.7)
hrr_max = 50
H = fuel_height[1] - fuel_height[0]
A_max = (fuel_xes[1] - fuel_xes[0]) ** 2 * 3.1415 / 4
config = rcsv('{}.ful'.format(name), sep=',')
alpha = triangular(*config.alpha_min, *config.alpha_max, mode=float(config.alpha_mode))
area = triangular(0, A_max)
print('alpha:{}, radius: {}'.format(alpha, (area / 3.1415) ** 0.5))
hrr = []
for i in range(0, int(self.t_end/120)):
hrr.extend([i / 60, round(H * alpha * (i ** 3) / 1000, 4)])
if hrr[-1] > hrr_max:
hrr[-1] = hrr_max
return hrr, area, fuel_height, fuel_xes, fuel_yes
def row_maker(self, name):
# change CSV file to applicable form
with open(name) as file:
lines = file.readlines()
if lines[0][:5] == 'Patch':
lines.pop(0)
lines.pop(1)
for i in range(len(lines[1:])):
lines[i + 1] = ','.join([str(i), lines[i + 1]])
with open(name, 'w') as file:
file.writelines(lines)
csv = rcsv(name)
row = []
csv.columns = csv.columns.str.strip().str.replace(' ', '_')
for v in csv.iloc[:, -1]:
if type(v) != str():
row.append(v)
return row
def split_mean(self, file):
data = {}
with open('temp.csv', 'w') as temp:
temp.write(file)
with open('temp.csv', 'r') as temp:
f = temp.readlines()
patch_no = f[0].split(' ')[0]
with open('temp.csv', 'w') as temp:
temp.writelines(f[1:])
file = rcsv('temp.csv')
file.drop(0, inplace=True)
print(file.columns)
# split according to Z
file.drop([' X', 'Z'], axis='columns', inplace=True)
# save in data dictionary as title:array(pandas df)
# title = 'Patch{} X={} Z={}'.format(patch_no, x, z)
# data{title:array}
return data
# calculating and writing ASET<RSET probability and uncertainty to the list
try:
p_evacfailed = (len(data.time_crit[data.time_crit <= int(rset)]) -
num_nocoll) / (len(data.time_crit) - num_nocoll)
save_list.append('P(ASET < RSET) = {}\n'.format(p_evacfailed))
except ZeroDivisionError:
save_list.append('unable to calculate P(ASET<RSET) and RMSE\n')
p_evacfailed = 0
err[1], save_list = uncertainty(save_list, p_evacfailed, n_iter)
with open('results.txt', 'w') as file:
file.writelines(save_list)
print('[OK] Results summary written to TXT file')
# draw charts
Charting(data, t_crit, rset, (p_collapse, p_evacfailed)).draw()
# check if uncertainty is low enough to stop calculations
if 0 < err[0] < 0.001 and 0 < err[1] < 0.001:
return True
else:
return False
if __name__ == '__main__':
user = user_config(argv[1])
summary(
rcsv('{}\\{}_results.csv'.format(user['results_path'],
user['case_title'])),
temp_crit(user['miu']), user['RSET'])