def get_line(spread, _type):
if _type == 'left' or _type == 'right':
return spread.spread[_type]['std_error']
elif _type == 'radius':
_, radius = calc_radius(spread, error=True)
return radius
def spread_plot(args):
"""Draw the spreading as a function of time."""
# Get colours and line styles from default
colours = get_colours(args.colour, len(args.spreading))
labels, draw_legend = get_labels(args.label, len(args.spreading))
linestyles = {}
linestyles['line'] = get_linestyles(args.linestyle, len(args.spreading))
linestyles['fit'] = get_linestyles(args.fitstyle, len(args.spreading),
'dashed')
# Create linear fitting function
fitfunc = lambda p, t, r: r - p[0] - p[1] * t
pinit = [1.0, 1/7]
# Find shift array for synchronisation
shift_array = get_shift(args.spreading, sync=args.sync)
impact_shift = get_shift(args.spreading, sync='impact')
# Create dicts for lists of fit constants (r = amp * t**index)
amp = {}
index = {}
ampError = {}
indexError = {}
ampMean = []
ampMeanError = []
indexMean = []
indexMeanError = []
for i, spread_list in enumerate(args.spreading):
amp[i] = []
index[i] = []
ampError[i] = []
indexError[i] = []
spread, full_data = combine_spread(spread_list, shift=shift_array[i])
spread.times = np.array(spread.times) - spread.times[0]
for k, _file in enumerate(spread_list):
data = Spread().read(_file)
data.times = np.array(data.times) - impact_shift[i][k]
# Get radius and domain
radius = {'real': np.array(calc_radius(data))}
domain = {'real': np.array(data.times)}
# Cut times outside of range
for j, time in enumerate(domain['real']):
if time > args.tend:
radius['real'] = radius['real'][:j]
domain['real'] = domain['real'][:j]
# Add logged values
radius['log'] = np.log10(radius['real'][1:])
domain['log'] = np.log10(domain['real'][1:])
# Cut in log range
for j, logt in enumerate(domain['log']):
if logt > args.tendlog:
radius['log'] = radius['log'][:j]
domain['log'] = domain['log'][:j]
# Fit constants to data
out = optimize.leastsq(fitfunc, pinit,
args=(domain['log'], radius['log']), full_output=1)
pfinal = out[0]
covar = out[1]
# Add unlogged constants to lists
amp[i].append(10**pfinal[0])
index[i].append(pfinal[1])
ampError[i].append(np.sqrt(covar[1][1]) * amp[i][-1])
indexError[i].append(np.sqrt(covar[0][0]))
if args.draw == 'log' and args.nomean:
plot_line(
line=radius['log'],
domain=domain['log'],
color=colours[i],
linestyle=linestyles['line'][i]
)
if not args.nofit:
plot_line(
line=out[0][0] + out[0][1] * domain['log'],
domain=domain['log'],
color=colours[i], linestyle=linestyles['fit'][i]
)
if args.draw == 'real' and args.nomean:
plot_line(
line=radius['real'],
domain=domain['real'],
color=colours[i], linestyle=linestyles['line'][i]
)
if not args.nofit:
plot_line(
line=amp[i][-1] * (domain['real']**index[i][-1]),
domain=domain['real'],
color=colours[i], linestyle=linestyles['fit'][i]
)
ampMean.append(np.mean(amp[i]))
ampMeanError.append(np.std(amp[i]) / np.sqrt(len(amp[i]) - 1))
indexMean.append(np.mean(index[i]))
indexMeanError.append(np.std(index[i]) / np.sqrt(len(index[i]) - 1))
if not args.nomean:
if args.draw == 'log':
plot_line(
line=np.log10(calc_radius(spread)),
domain=np.log10(spread.times),
label=labels[i],
color=colours[i], linestyle=linestyles['line'][i]
)
if not args.nofit:
plot_line(
line=(np.log10(ampMean[i])
+ indexMean[i] * np.log10(spread.times)),
domain=np.log10(spread.times),
label='C=%.2f, n=%.2f'%(ampMean[i], indexMean[i]),
color=colours[i], linestyle=linestyles['fit'][i]
)
if args.draw == 'real':
plot_line(
line=calc_radius(spread),
domain=spread.times,
label=labels[i],
color=colours[i], linestyle=linestyles['line'][i]
)
if not args.nofit:
plot_line(
line=ampMean[i] * (domain['real']**indexMean[i]),
domain=domain['real'],
label='C=%.2f, n=%.2f'%(ampMean[i], indexMean[i]),
color=colours[i], linestyle=linestyles['fit'][i]
)
plt.title(args.title, fontsize='medium')
plt.axis('normal')
plt.legend()
# Default xlabel and xlims based on draw method
if args.draw == 'real':
if args.ylabel == None:
args.ylabel = "Spread radius (nm)"
if args.xlabel == None:
args.xlabel = "Time (ps)"
if (args.tend and args.tendlog) < np.inf:
plt.xlim([None, min(args.tend, 10**args.tendlog)])
elif args.draw == 'log':
if args.ylabel == None:
args.ylabel = "log10 of radius (in nm)"
if args.xlabel == None:
args.xlabel = "log10 of time (in ps)"
if (args.tend and args.tendlog) < np.inf:
plt.xlim([None, min(args.tend, args.tendlog)])
plt.xlabel(args.xlabel, fontsize='medium')
plt.ylabel(args.ylabel, fontsize='medium')
if args.xlim:
plt.xlim(args.xlim)
# Print collected output
print("Fitting spread radius 'R' of input file sets to power law functions "
"of time 't' as 'R = C * (t ** n)' and taking means:")
for i, _ in enumerate(amp):
print()
# If nomean, print individual line values
if args.nomean:
for values in zip(amp[i], ampError[i], index[i], indexError[i]):
print("%f +/- %f" % (values[0], values[1]), end=', ')
print("%f +/- %f" % (values[2], values[3]))
# Print mean values
if args.nomean:
print(" -> ", end='')
print("C = %f +/- %f" % (ampMean[i], ampMeanError[i]))
if args.nomean:
print(" -> ", end='')
print("n = %f +/- %f" % (indexMean[i], indexMeanError[i]))
# Finish by saving and / or showing
if args.save:
plt.savefig(args.save)
if args.draw != 'off':
plt.show()
return None
def get_line(spread, _type):
if _type == 'left' or _type == 'right':
return spread.spread[_type]['val']
elif _type == 'radius':
return calc_radius(spread)
