def main():
total = 0
data = getdata("input.txt")
for entry in data:
fuel = calcfuel(entry)
total_for_entry = fuel
while calcfuel(fuel) > 0:
fuel = calcfuel(fuel)
total_for_entry += fuel
total += total_for_entry
return total
def dashboard():
try:
p = []
p = getdata()
#print(p)
values = map(int, p)
appl = appplication()
infrs = infrastructure()
sys = sysbuild()
db = Database()
NSA = NetSecArch()
FN = FND()
CodeR = CR()
SecCo = SC()
print(appl)
print(infrs)
legend = 'Vulnerability frequency By type'
legend2 = 'Web application vulnerabilities'
labels = ["Critical", "High", "Medium", "Low", "Informational"]
labels2 = ["Critical", "High", "Medium", "Low", "Informational"]
return render_template('template.html',
value1=p[0],
value2=p[1],
value3=p[2],
value4=p[3],
value5=p[4],
values=values,
labels=labels,
legend=legend,
labels2=labels2,
appl=appl,
infr=infrs,
legend2=legend2)
except Exception as e:
print(e)
def acquireData(self):
self.data = functions.getdata(self.spectrometer)
return self.data
def main():
lines = getdata()
intersections = getintersections(lines[0], lines[1])
closest = getclosestintersection(intersections)
header_data = infile.readline()
finally:
infile.close()
header_data = header_data.split()
header_data = [item.lower() for item in header_data]
# get data from file
# important to notice: pandas.read_csv are faster than numpy.genfromtxt
data = pd.read_csv(file, delimiter='\t', header=1,
names=header_data, engine='c', decimal=',')
# data = data[data != 0]
# initialize Checkbarlist for dialog later
Checkbarlist = ['save plots?']
[puv, puv_time, pv, time, filt_dat, current, r_spec] = getdata(data)
# plt.plot(r_spec)
# plt.show()
# write data in seperate vatiables for better readability
for i in range(0, len(header_data)):
if header_data[i] == 'time':
time = np.array(data['time'], dtype=pd.Series)
elif header_data[i] == 'current':
# current = np.array(data['current'], dtype=pd.Series)
Checkbarlist.append('current')
elif header_data[i] == 'r_spec':
# r_spec = np.array(data['r_spec'], dtype=pd.Series)
Checkbarlist.append('r_spec')
elif header_data[i] == 'puv':
# puv = np.array(data['puv'], dtype=pd.Series)
total = dist()
totaltime = timerun()
adjusted = adjust(totaltime)
finalpace = calcpace(adjusted, total)
convertedpace = converttime(finalpace)
impress = toprint(convertedpace)
elif option == '2':
total = dist()
totaltime = timerun()
adjusted = adjust(totaltime)
calctemp = predictrun(adjusted, total)
convertedtemprun = converttime(calctemp)
impress = toprint(convertedtemprun)
elif option == '3':
createtable()
thedate = date()
total = dist()
totaltime = timerun()
adjusted = adjust(totaltime)
finalpace = calcpace(adjusted, total)
convertedpace = converttime(finalpace)
obs = observations()
escrever = setdata(thedate, total, totaltime, convertedpace, obs)
elif option == '4':
getdata()
deletedata()
elif option == '5':
getdata()
else:
invalidoption_text()
else:
sys.exit('It is recommened to use Linux! (or Windows if you have to)')
# check if user aborted file selection
if file == '':
sys.exit('no file to load data from!')
# datalength = list(range(100))
# get data frprofile nuom file
# important to notice: pandas.read_csv are faster than numpy.genfromtxt
data = pd.read_csv(file, delimiter='\t', header=0, engine='c', decimal=',')
# use the rawdata on function getdata to split profiles and smooth it
[raw_dat, raw_time, raw_pv, raw_pv_time, filt_dat, current_dat,
r_spec_dat] = getdata(data)
# save raw_d and raw_pv for further computations
np.save('filt_dat', filt_dat)
np.save('raw_dat', raw_dat)
np.save('raw_pv', raw_pv)
np.save('raw_time', raw_time)
np.save('raw_pv_time', raw_pv_time)
# exit()
# from compute_delta_cp_and_e_ht_as_function import delta_cp_and_e_ht
# from functions import delta_cp_and_e_ht
# print(np.shape(raw_time))
# exit()
(epsht, delta_cp, f_epsht, f_delta_cp, c_puv) = \
def main(): lines = getdata() intersections = getintersections(lines[0], lines[1]) print(leaststeps(lines, intersections))
def main():
data = getdata("input.txt")
total = 0
for entry in data:
total += calcfuel(entry)
return total