def data_job_retention(nSelected,nRunOver):
"""Calculate the retention for a data job"""
from uncertainties import ufloat as u
from math import sqrt as s
nsel = u((nSelected,s(nSelected)))
nrun = u((nRunOver,s(nRunOver)))
out = nsel/nrun
print "(", 100*out, ") %"
return out
def mc_job_selection_efficiency(DecProdEff,DecProdEffErr,nSelected,nRunOver):
"""Calculate the selection efficiency for a MC job"""
from uncertainties import ufloat as u
from math import sqrt as s
e_dpc = u((DecProdEff,DecProdEffErr))
nsel = u((nSelected,s(nSelected)))
nrun = u((nRunOver,s(nRunOver)))
out = e_dpc*nsel/nrun
print "(", 100*out, ") %"
return out
time_co2[i].append(array[:,1])
#%%
osc_P_ar, osc_T_ar, time_array_ar, fit_ar, fit_err_ar = get_data_fit(P_ar, time_ar)
osc_P_co2, osc_T_co2, time_array_co2, fit_co2, fit_err_co2 = get_data_fit(P_co2, time_co2)
#%%
fit_wErr = []
for i in range(len(fit_ar)):
fit_wErr.append([])
for j in range(len(fit_ar[i])):
fit_wErr[i].append([])
for k in range(len(fit_ar[i][j])):
fit_wErr[i][j].append(u(fit_ar[i][j][k], fit_err_ar[i][j][k]))
for i in range(len(fit_co2)):
fit_wErr.append([])
for j in range(len(fit_co2[i])):
fit_wErr[i+3].append([])
for k in range(len(fit_co2[i][j])):
fit_wErr[i+3][j].append(u(fit_co2[i][j][k], fit_err_co2[i][j][k]))
fit_wErr_avg = []
#P0, c, k, omega, phi
for i in range(len(fit_wErr)):
fit_wErr_avg.append([])
for j in range(len(fit_wErr[i])):
param = [[], [], [], [], []]
for k in range(5):
plt.plot(new_x[29:64], 5.177800237*(new_x[29:64]-0.029637)+0.029637, color = 'tab:red', label = 'Stripping Line')
plt.xlabel('X - Liquid Mole Fraction Ethanol')
plt.ylabel('Y - Vapor Mole Fraction Ethanol')
plt.axis('equal')
plt.xticks(np.arange(0,1.1,0.1))
plt.yticks(np.arange(0,1.1,0.1))
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.grid()
plt.legend()
#plt.savefig('McCabe_Thiele_R4.png',dpi=300,bbox_inches='tight')
#%%
bottom = np.array(984.7, 990.6, 989.7, 990.4])
bottomT = [17.1, 15.7, 12.3, 11.9]
top = [u(830.4, 1), u(829.4, 1), u(821.6, 1), u(826, 1)]
topT = [u(13.9, 0.2), u(16.2, 0.2), u(20.7, 0.2), u(18.6, 0.2)]
#%%
molfrac = [[], []]
molfrac_error = [[],[]]
for i in range(4):
molfrac[0].append(true_proof((bottomT[i]*9/5)+32, bottom[i].value)[1])
# molfrac[1].append(true_proof((topT[i]*9/5)+32, top[i])[1])
#%%
# R = 2
R2_trays = [[0.8312, 0.8951, 0.9593, 0.976],[0.83, 0.9, 0.963, 0.9777]]
R2_temp = [[21, 18.8, 17, 15.1],[21.6, 17, 15.6, 14.2]]
R2_molfrac = [[], []]
for i in range(2):
def num(self, *args):
if self.calibration_uncertainties:
return u(*args)
else:
return args[0]