def viscLifetimes(viscs):
from PYME.Analysis._fithelpers import *
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 stateLifetimes(spec, concs):
from PYME.Analysis._fithelpers import FitModel
figure()
constants={'q':10e-3, 'O2':0.1*air_sat_O2_conc}
I = Stimulus(0, [], [])
t = 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':('I', '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)
loglog(concs, tXs, label='X', lw=2)
loglog(concs, tRs, label='R', lw=2)
plot([nConc, nConc], ylim(), 'k--')
ylabel('Time constant [s]')
xlabel('[%s]' % spec)
legend()
def dyeConc(concs):
from PYME.Analysis._fithelpers import FitModel
plt.figure()
constants = {'q': 0, 'O2': 0}
I = Stimulus(I0, [], [])
t = np.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])
plt.loglog(concs, tXs, label='X', lw=2)
plt.loglog(concs, tRs, label='R', lw=2)
plt.ylabel('Time constant [$s^{-1}$]')
plt.legend()
def FitJumpSizeDist(velocities, startParams, dT):
from PYME.Analysis._fithelpers import FitModel
N = len(velocities)
plt.figure()
h, b, _ = plt.hist(velocities, 200)
x = b[1:]
dx = x[1] - x[0]
r = FitModel(jumpDistModel, startParams, h, x, N, dT, dx)
plt.plot(x, jumpDistModel(r[0], x, N, dT, dx), lw=2)
fT = 0
for i in range(len(startParams) / 2):
D, f = r[0][(2 * i):(2 * i + 2)]
plt.plot(x,
N * f * jumpDistProb(x, D, dT) * dx,
'--',
lw=2,
label='D = %3.2g, f=%3.2f' % (D, f))
fT += f
D = r[0][-1]
plt.plot(x,
N * (1 - fT) * jumpDistProb(x, D, dT) * dx,
'--',
lw=2,
label='D = %3.2g, f=%3.2f' % (D, (1 - fT)))
plt.xlabel('Jump Size [nm]')
plt.ylabel('Frequency')
plt.legend()
return r
def msdinfo(self):
if not '_msdinfo' in dir(self):
from PYME.Analysis.points.DistHist import msdHistogram
from PYME.Analysis._fithelpers import FitModel
x = self['x']
y = self['y']
t = self['t']
dt = self.pipeline.mdh.getOrDefault('Camera.CycleTime', 1.0)
nT = int((t.max() - t.min()) / 2)
h = msdHistogram(x, y, t, nT)
t_ = dt * np.arange(len(h))
res = FitModel(powerMod2D, [h[-1] / t_[-1], 1.], h[1:], t_[1:])
D, alpha = res[0]
#calculate the diffusion coefficient for normal (not anomolous diffusion)
#restrict to the first 100 bins to try and minimise effects of drift
#fit an offset to take localization precision into account
D_ = float(
np.linalg.lstsq(
np.vstack([t_[1:100], np.ones_like(t_[1:100])]).T,
h[1:100])[0][0] / 4)
self._msdinfo = {
't': t_,
'msd': h,
'D': D,
'alpha': alpha,
'Dnormal': D_
}
return self._msdinfo
def FitTrace(tr, mdh):
# from pylab import *
import matplotlib.pyplot as plt
#tr = (ds - mdh.getEntry('Camera.ADOffset')).sum(1).sum(0)
cycTime = mdh.getEntry('Camera.CycleTime')
t = cycTime * np.arange(len(tr))
plt.figure()
plt.plot(t, tr)
yp = -.1 * tr.max()
y1 = tr[61:80]
r1 = FitModel(kinModels.e3mod, [y1[0], 1.0, 0], y1,
cycTime * np.arange(len(y1)))
plt.plot(t[61:80],
kinModels.e3mod(r1[0], cycTime * np.arange(len(y1))),
lw=2)
plt.text(t[65], yp, '%3.2fs' % r1[0][1])
y2 = tr[81:1500]
r2 = FitModel(intensProf.eMod5, [y2[-1], -50, 50], y2,
cycTime * np.arange(len(y2)))
plt.plot(t[81:1500],
intensProf.eMod5(r2[0], cycTime * np.arange(len(y2))),
lw=2)
plt.text(t[700], yp, '%3.2fs' % r2[0][2])
y3 = tr[1501:1600]
r3 = FitModel(kinModels.e3mod, [y3[0], 1.0, 0], y3,
cycTime * np.arange(len(y3)))
plt.plot(t[1501:1600],
kinModels.e3mod(r3[0], cycTime * np.arange(len(y3))),
lw=2)
plt.text(t[1520], yp, '%3.2fs' % r3[0][1])
# y4 = tr[1601:2500]
# r4 = FitModel(intensProf.eMod5, [y4[-1], -50, 50], y4, cycTime*arange(len(y4)))
#
# plot(t[1601:2500], intensProf.eMod5(r4[0], cycTime*arange(len(y4))), lw=2)
# text(t[1900], yp, '$\\tau = %3.2fs$'%r4[0][2])
y5 = tr[2502:2600]
r5 = FitModel(kinModels.e3mod, [y5[0], 1.0, 0], y5,
cycTime * np.arange(len(y5)))
plt.plot(t[2502:2600],
kinModels.e3mod(r5[0], cycTime * np.arange(len(y5))),
lw=2)
plt.text(t[2520], yp, '%3.2fs' % r5[0][1])
y6 = tr[3502:3600]
r6 = FitModel(kinModels.e3mod, [y6[0], 1.0, 0], y6,
cycTime * np.arange(len(y6)))
plt.plot(t[3502:3600],
kinModels.e3mod(r6[0], cycTime * np.arange(len(y6))),
lw=2)
plt.text(t[3520], yp, '%3.2fs' % r6[0][1])
