# been supplied.
has_theory = False
if radius != None and viscosity != None:
diffusion_theory = bmc.theoretical(radius, viscosity, temperature)
has_theory = True
# Open pickle files and compute the weigted means.
info_dicts = []
for arg in args:
with open(os.path.abspath(arg)) as pkl:
info_dicts.append(cPickle.load(pkl))
ave_diffusion = get_diffusion(info_dicts, 'ave')
fit_diffusion = get_diffusion(info_dicts, 'fit')
ave_diffusion, err_ave_diffusion = bmc.mean_with_err(*ave_diffusion)
fit_diffusion, err_fit_diffusion = bmc.mean_with_err(*fit_diffusion)
if outfile != None:
diffusion = dict()
diffusion['diffusion'] = fit_diffusion
diffusion['uncertainty'] = err_fit_diffusion
print 'Saving diffusion coefficient to', outfile + '.'
with open(outfile, 'w') as pkl:
cPickle.dump(diffusion, pkl)
# Print some results.
print
if has_theory:
print 'Particle radius:', radius, 'm'
print 'Viscosity: %g Ps' % viscosity
if len(args) == 0:
print 'No input pickles supplied.'
usage(1)
# Open pickle files and compute the weigted means.
info_dicts = []
for arg in args:
with open(os.path.abspath(arg)) as pkl:
info_dicts.append(cPickle.load(pkl))
# Compute the mean RMS velocity
vrms = get_values(info_dicts, 'vel_rms')
work = get_values(info_dicts, 'mean_work')
work_rate = get_values(info_dicts, 'work_rate')
ave_vrms, err_vrms = bmc.mean_with_err(*vrms)
ave_work, err_work = bmc.mean_with_err(*work)
ave_work_rate, err_work_rate = bmc.mean_with_err(*work_rate)
atp_energy = 0.4 * 20.5 / spc.N_A
num_myosin = ave_work / atp_energy
err_myosin = err_work / atp_energy
print 'RMS speed:', ave_vrms, 'm/s'
print 'Uncertainty:', err_vrms, 'm/s'
print
print 'Average work:', ave_work, 'J'
print 'Uncertainty:', err_work, 'J'
print
print 'Average number of myosin motors:', num_myosin
print 'Uncertainty:', err_myosin
if radius != None and viscosity != None:
diffusion_theory = bmc.theoretical(radius, viscosity, temperature)
has_theory = True
# Open and read the data file.
all_tracks = bmc.read_data(data_file)
# Only accept tracks where there is displacement in each step.
all_tracks = filter(lambda t: all(t[6][1:]), all_tracks)
nparticles = len(all_tracks)
# Compute the diffusions.
time_step = 0.0330033 # The time step isn't recorded uniformly.
all_diffusion = map(get_diffusion_ave(time_step), all_tracks)
diff_list = [d[0] for d in all_diffusion]
diff_err_list = [d[1] for d in all_diffusion]
diffusion, err_diffusion = bmc.mean_with_err(diff_list, diff_err_list)
# Create average displacements and fit a curve
ave_dispsq = bmc.average_dispsq(all_tracks, 50)
num_avg = len(ave_dispsq[0])
time = np.linspace(1, num_avg, num_avg) * time_step
popt, pcov = spo.curve_fit(linear, time, ave_dispsq[0], sigma=ave_dispsq[1])
fit_data = popt[0] * time + popt[1]
fit_diff = popt[0] / 4.0
fit_err = np.sqrt(pcov[0][0]) / 4.0
axis_bounds = [min(time), max(time)]
axis_bounds += [0, max(max(fit_data), max(ave_dispsq[0]))]
# Create a dictionary to save to and save it to a pickle.
if pkl_file != None:
brownian_info = dict()
