def get_logss_gradient_critical_rs(c_length, c_particle, init_b, final_b, tg, flow, v_m, v_d, lssparam , crit_res=1.4):
"""Function to estimate critical couples and resolutions for logarithmic gradient mode
N.B.: The molecule id correspond to the exact position in lssparam.
"""
if final_b > 1 or final_b < 0 or isnan(final_b) or init_b < 0 or init_b > 1 or isnan(init_b) or tg < 0 or isnan(tg):
return None, None
else:
trtab = []
logssmol = SSGenerator(None, None, None, t0, v_d, flow)
i = 0
for row in lssparam:
lss_logkw = float(row[0])
lss_s = float(row[1])
W = get_logss_peak_width(c_length, c_particle, lss_logkw, lss_s, init_b, final_b, tg, flow, v_m, v_d, None)
try:
tr_tmp = logssmol.logrtpred(lss_logkw, lss_s, tg, init_b, final_b, t0, td)
if tr_tmp < t0 or isnan(tr_tmp):
trtab.append([t0, W, i])
else:
trtab.append([tr_tmp, W, i])
except:
trtab.append([t0, W, i])
i += 1
trtab = sorted(trtab, key=lambda x:x[0])
if trtab[-1][0] > tg: #if the maximumt time is over the time gradient Error!!
return None, None
else:
lstcc = []
lowestrs = None
for i in range(1, len(trtab)):
# Multiply by 1.7 to obtain the peak width at base.
width1 = trtab[i][1] * 1.7
width2 = trtab[i-1][1] * 1.7
tr1 = trtab[i-1][0]
tr2 = trtab[i][0]
rs = (2.*(tr2-tr1)) / (width1+width2)
if rs < crit_res:
# Save the original id of lssparam
lstcc.append([trtab[i-1][2], trtab[i][2], rs])
if lowestrs != None:
if rs < lowestrs:
lowestrs = rs
else:
continue
else:
lowestrs = rs
else:
continue
return lstcc, lowestrs
def get_logss_gradient_critical_selectivity(c_lenght, c_particle, init_b, final_b, tg, flow, v_m, v_d, lssparam, crit_alpha=1.1):
"""Function to estimate critical couples and resolutions for logarithmic gradient mode
N.B.: The molecule id correspond to the exact position in lssparam.
"""
if final_b > 1 or final_b < 0 or isnan(final_b) or init_b < 0 or init_b > 1 or isnan(init_b) or tg < 0 or isnan(tg):
return None, None
else:
t0 = v_m/flow
td = v_d/flow
N = (c_lenght*10000.)/(3.4*c_particle)
trtab = []
logssmol = SSGenerator(None, None, None, t0, v_d, flow)
i = 0
for row in lssparam:
lss_logkw = float(row[0])
lss_s = float(row[1])
try:
tr_tmp = logssmol.logrtpred(lss_logkw, lss_s, tg, init_b, final_b, t0, td)
if tr_tmp < t0 or isnan(tr_tmp):
return None, None # these condition are not ok for selectivity calculations
else:
trtab.append([tr_tmp, i])
except:
return None, None # these condition are not ok for selectivity calculations
i += 1
if len(trtab) == len(lssparam):
trtab = sorted(trtab, key=lambda x:x[0])
lstcc = []
lowest_alpha = (trtab[1][0] - t0) / (trtab[0][0]-t0)
for i in range(1, len(trtab)):
alpha = (trtab[i][0]-t0) / (trtab[i-1][0]-t0)
#print trtab[i][0], trtab[i-1][0], t0, (trtab[i][0] - t0), (trtab[i-1][0]-t0)
if alpha < lowest_alpha:
lowest_alpha = alpha
if alpha < crit_alpha:
# Save the original id of lssparam
lstcc.append([trtab[i-1][1], trtab[i][1], alpha])
return lstcc, lowest_alpha
else:
return None, None
