def dyeConc(concs):
from PYME.Analysis._fithelpers import FitModel
figure()
constants={'q':0, 'O2':0}
I = Stimulus(I0, [], [])
t = linspace(1, 1e6, 1000)
tXs = []
tRs = []
s = System(r,constants=constants, ties={'S1':('I', 'S0')},stimulae={'I':I})#
s.GenerateGradAndJacCode()
for i in range(len(concs)):
s.initialConditions['S0'] = concs[i] #conc of fluorophores on an antibody ~ 100M
res = s.solve(t)
r1 = FitModel(emod, [1e-3, 3], res['X'][t>1e6], t[t>1e6]/1e6 - 1)
tXs.append(r1[0][1])
r1 = FitModel(emod, [1e-3, 3], res['R'][t>1e6], t[t>1e6]/1e6 - 1)
tRs.append(r1[0][1])
loglog(concs, tXs, label='X', lw=2)
loglog(concs, tRs, label='R', lw=2)
ylabel('Time constant [$s^{-1}$]')
legend()
def stateLifetimes3(spec, concs, r, labelAddition = '', constants={'q':10e-3, 'O2':0.1*air_sat_O2_conc}, **kwargs):
lineSpec = {'lw':2}
lineSpec.update(kwargs)
#figure()
#constants={'q':10e-3, 'O2':0.1*air_sat_O2_conc}
I = Stimulus(I0, [9e4], [0])
t = linspace(1, 1e5, 10000)
tXs = []
tRs = []
nConc = constants[spec]
rates = []
initConc = 1e-3
s = System(r,constants={}, ties={'S1':('I', 'S0')},stimulae={'I':I})#
s.GenerateGradAndJacCode()
s.initialConditions['S0'] = 1e-3 #conc of fluorophores on an antibody ~ 100M
#s.initialConditions['X'] = initConc
#s.initialConditions['R'] = initConc
s.initialConditions['q'] = constants['q']
s.initialConditions['O2'] = constants['O2']
for i in range(len(concs)):
s.initialConditions[spec] = concs[i]
res, r = s.solve(t, True)
#print res[-1].dtype
#print res[-1].view('9f8')
rates.append(r[-1])
rates = hstack(rates)
#plot(concs, 1e-6/(-rates['X']/initConc))
loglog(concs, 1e-6/(-rates['X']/initConc), c = 'b', label='X' + labelAddition, **lineSpec)
loglog(concs, 1e-6/(-rates['R']/initConc), c = 'g', label='R' + labelAddition, **lineSpec)
plot([nConc, nConc], ylim(), 'k--')
ylabel('Dark state lifetime [s]')
xlabel('[%s]' % spec)
legend()
def stateLifetimes(spec, concs):
from PYME.Analysis._fithelpers import *
plt.figure()
constants = {'q': 10e-3, 'O2': 0.05 * air_sat_O2_conc}
I = Stimulus(0, [], [])
t = np.linspace(1, 10e6, 1000)
tXs = []
tRs = []
nConc = constants[spec]
for i in range(len(concs)):
constants[spec] = concs[i]
s = System(r,
constants=constants,
ties={'S1': ('0', 'S0')},
stimulae={'I': I}) #
s.GenerateGradAndJacCode()
#s.initialConditions['S0'] = 1e-3 #conc of fluorophores on an antibody ~ 100M
s.initialConditions['X'] = 1e-3
s.initialConditions['R'] = 1e-3
res = s.solve(t)
r1 = FitModel(emod, [1e-3, 3], res['X'], t / 1e6)
tXs.append(r1[0][1])
r1 = FitModel(emod, [1e-3, 3], res['R'], t / 1e6)
tRs.append(r1[0][1])
print(max(tXs[-1], tRs[-1]))
#t = linspace(1, 5e6*max(max(tXs[-1], tRs[-1]), 1e-3), 1000)
plt.loglog(concs, tXs, label='X', lw=2)
plt.loglog(concs, tRs, label='R', lw=2)
plt.plot([nConc, nConc], plt.ylim(), 'k--')
plt.ylabel('Time constant [s]')
plt.xlabel('[%s]' % spec)
plt.legend()
def viscLifetimes(viscs):
from PYME.Analysis._fithelpers import FitModel
plt.figure()
constants = {'q': 1e-6, 'O2': 0.1 * air_sat_O2_conc}
I = Stimulus(I0, [1e6], [0])
tXs = []
tRs = []
for i in range(len(viscs)):
visc = viscs[i]
visc2 = visc
if visc <= 10:
t = np.linspace(1, 10e6, 10000)
else:
t = np.linspace(1, 10e6, 1000)
r = genReactionScheme(visc, visc2)
s = System(r,
constants=constants,
ties={'S1': ('I', 'S0')},
stimulae={'I': I}) #
s.GenerateGradAndJacCode()
s.initialConditions[
'S0'] = 1e-3 #conc of fluorophores on an antibody ~ 100M
res = s.solve(t)
r1 = FitModel(emod, [1e-3, 3], res['X'][t > 1e6], t[t > 1e6] / 1e6 - 1)
tXs.append(r1[0][1])
r1 = FitModel(emod, [1e-3, 3], res['R'][t > 1e6], t[t > 1e6] / 1e6 - 1)
tRs.append(r1[0][1])
plt.loglog(viscs, tXs, label='X', lw=2)
plt.loglog(viscs, tRs, label='R', lw=2)
plt.ylabel('Time constant [$s^{-1}$]')
plt.legend()
def stateLifetimes2(spec,
concs,
r,
labelAddition='',
constants={
'q': 10e-3,
'O2': 0.1 * air_sat_O2_conc
},
**kwargs):
lineSpec = {'lw': 2}
lineSpec.update(kwargs)
#figure()
#constants={'q':10e-3, 'O2':0.1*air_sat_O2_conc}
I = Stimulus(0, [], [])
t = np.linspace(1, 10e6, 1000)
tXs = []
tRs = []
nConc = constants[spec]
rates = []
initConc = 1e-3
s = System(r, constants={}, ties={'S1': ('0', 'S0')}, stimulae={'I': I}) #
s.GenerateGradAndJacCode()
#s.initialConditions['S0'] = 1e-3 #conc of fluorophores on an antibody ~ 100M
s.initialConditions['X'] = initConc
s.initialConditions['R'] = initConc
s.initialConditions['q'] = constants['q']
s.initialConditions['O2'] = constants['O2']
for i in range(len(concs)):
s.initialConditions[spec] = concs[i]
rates.append(
s.GradFcn(0, s.initialConditions.view('f8')).view(s.dtype))
rates = np.hstack(rates)
plt.loglog(concs,
1e-6 / (-rates['X'] / initConc),
c='b',
label='X' + labelAddition,
**lineSpec)
plt.loglog(concs,
1e-6 / (-rates['R'] / initConc),
c='g',
label='R' + labelAddition,
**lineSpec)
plt.plot([nConc, nConc], plt.ylim(), 'k--')
plt.ylabel('Dark state lifetime [s]')
plt.xlabel('[%s]' % spec)
plt.legend()
k15 / visc), #postulated permanent bleaching pathway
Reaction(
'R + O2 <-> S0 + HO2', 5e2 / visc
), #oxidation of R by 02 - rate constant from [kaske & linquist `64]
]
r = genReactionScheme(visc, visc2)
#Intensity as a fraction of saturation intensity
I0 = 1e-1
#intensity function
#I = Stimulus(1e-3*I0, 1e6*np.array([1, 3, 4, 5, 6, 8, 9, 15, 16, 25, 26, 50, 51, 100]),
# [0, I0, 0, I0, 0, I0, 0, I0, 0, I0, 0, I0, 0, I0])
I = Stimulus(I0, [], [])
#s = System(r,constants={'I':I0, 'q':1e-6, 'O2':0.1*air_sat_O2_conc}, ties={'S1':('I', 'S0')})#
constants = {'q': 5e-7, 'O2': (0.5e-5) * air_sat_O2_conc}
#constants = {}
#constants={'q':10e-3}
s = System(r, constants=constants, ties={'S1': ('I', 'S0')}, stimulae={'I':
I}) #
s.GenerateGradAndJacCode()
s.initialConditions['S0'] = 1e-6 #conc of fluorophores on an antibody ~ 100M
#s.initialConditions['q'] = .25e-3
#s.initialConditions['O2'] = .1*air_sat_O2_conc
exPower = I0 * k_emmision / excitations_per_W_per_cm2_per_us
print(u'Excitation Power: %3.2g W/cm\xB2' % (exPower))
print(u' = %3.2g mW over a 15 \u03BCm field' %