def get_cc(experiment):
    ob = experiment.objectives['elongation_rate']

    s = slicing.Slicer.from_objective_bind(ob)

    rs, names, concentration_mesh = s.minimum_values('atp_concentration')
    cc = zero_crossings(concentration_mesh[0], rs)[0]

    return cc
Пример #2
0
def get_cc(experiment):
    ob = experiment.objectives['elongation_rate']

    s = slicing.Slicer.from_objective_bind(ob)

    rs, names, concentration_mesh = s.minimum_values('atp_concentration')
    cc = zero_crossings(concentration_mesh[0], rs)[0]

    return cc
Пример #3
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def D_vs_concentration(session, cc_scale=False, **kwargs):
#    e = session.get_experiment('critical_concentration')
    e = session.get_experiment('fujiwara_2002')
    D_ob = e.objectives['final_diffusion_coefficient']
    D_s = slicing.Slicer.from_objective_bind(D_ob)

    Ds, name, concentration_mesh = D_s.minimum_values('atp_concentration')

    j_ob = e.objectives['final_elongation_rate']
    j_s = slicing.Slicer.from_objective_bind(j_ob)

    js, name, concentration_mesh = j_s.minimum_values('atp_concentration')

    concentration_mesh = concentration_mesh[0]

    if cc_scale:
        cc = zero_crossings(concentration_mesh, js)[0]
        concentration_mesh = numpy.array(concentration_mesh) / cc
        print 'cc =', cc

#    pylab.figure()
    pylab.subplot(2,1,1)
    measurements.line((concentration_mesh, Ds), **kwargs)
    if cc_scale:
        pylab.axvline(x=1, color='black')
    pylab.ylabel('Tip Diffusion Coefficient (mon**2 /s)')

    pylab.subplot(2,1,2)
    zero_concentrations = [concentration_mesh[0], concentration_mesh[-1]]
    zero_values = [0, 0]

    measurements.line((zero_concentrations, zero_values))
    measurements.line((concentration_mesh, js), **kwargs)
    if cc_scale:
        pylab.axvline(x=1, color='black')
    pylab.ylabel('Elongation Rate (mon /s )')

    if cc_scale:
        pylab.xlabel('[G-ATP-actin] (critical concentrations)')
    else:
        pylab.xlabel('[G-ATP-actin] (uM)')