def copoly_halftime_plot(filename):
adp_halftimes = data.load_data('results/adp_copoly_halftimes.dat')
nh_halftimes = data.load_data('results/nh_copoly_halftimes.dat')
adp_v_halftimes = data.load_data('results/adp_copoly_halftimes_vectorial.dat')
nh_v_halftimes = data.load_data('results/nh_copoly_halftimes_vectorial.dat')
fractions, combined_data = _combine_data(adp_halftimes, nh_halftimes)
vfractions, vcombined_data = _combine_data(adp_v_halftimes, nh_v_halftimes)
with contexts.basic_figure(filename,
y_label=r'Halftime [s]',
logscale_y=True) as axes:
for local_data, lt, in zip(combined_data, LINETYPES):
contexts.plot(axes, 'plot', fractions, local_data, lt)
for vld in vcombined_data:
contexts.plot(axes, 'plot', vfractions, vld, 'k-')
# new_x_tick_labels = [10, 5, 0, 5, 10]
#
# axes.set_xticks([-10, -5, 0, 5, 10])
new_x_tick_labels = [50, 40, 30, 20, 10, 0, 10]
axes.set_xticks([-50, -40, -30, -20, -10, 0, 10])
axes.set_xticklabels(new_x_tick_labels)
axes.set_ylim(10, 10**5)
axes.text(X_LABEL_PADDING, X_LABEL_MARGIN, 'ADP-actin [%]',
verticalalignment='top', horizontalalignment='left',
transform=axes.transAxes)
axes.text(1 - X_LABEL_PADDING, X_LABEL_MARGIN, 'NH-actin [%]',
verticalalignment='top', horizontalalignment='right',
transform=axes.transAxes)
# \rho_d arrows
axes.annotate(INCREASING_RHO_TEXT,
xy=(-HT_ARROW_X_OFFSET, TIMECOURSE_HALFTIME),
xytext=(-HT_ARROW_X_OFFSET, 6e3),
arrowprops={'facecolor': 'black',
'arrowstyle': '->'},
horizontalalignment='center',
verticalalignment='top',
size=settings.SMALL_FONT_SIZE)
axes.annotate(INCREASING_RHO_TEXT,
xy=(HT_ARROW_X_OFFSET, TIMECOURSE_HALFTIME),
xytext=(HT_ARROW_X_OFFSET, 17),
arrowprops={'facecolor': 'black',
'arrowstyle': '->'},
horizontalalignment='center',
verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
axes.axvline(0, 0, 1, linestyle=':', linewidth=0.5, color='k')
def _timecourse(fnc, f_filename, p_filename, sim_filename, output_filename):
ftimes, fdata = data.load_data(f_filename)
ptimes, pdata = data.load_data(p_filename)
sim_results = data.load_data(sim_filename)
stimes = numpy.array(sim_results[0])
slengths = numpy.array(sim_results[1])
sadppi = numpy.array(sim_results[3])
stimes /= 60
slengths *= fnc / ACTIN_CONCENTRATION
sadppi *= fnc / ACTIN_CONCENTRATION
with contexts.basic_figure(output_filename,
x_label='Time [min]',
y_label='Polymer Fraction') as axes:
contexts.plot(axes, 'plot', ftimes, fdata, 'k.')
contexts.plot(axes, 'plot', ptimes, pdata, 'r.')
contexts.plot(axes, 'plot', stimes, slengths, 'k-')
contexts.plot(axes, 'plot', stimes, sadppi, 'r-')
axes.set_xlim(0, 35)
def _timecourse(fnc, f_filename, p_filename, sim_filename,
output_filename):
ftimes, fdata = data.load_data(f_filename)
ptimes, pdata = data.load_data(p_filename)
sim_results = data.load_data(sim_filename)
stimes = numpy.array(sim_results[0])
slengths = numpy.array(sim_results[1])
sadppi = numpy.array(sim_results[3])
stimes /= 60
slengths *= fnc / ACTIN_CONCENTRATION
sadppi *= fnc / ACTIN_CONCENTRATION
with contexts.basic_figure(output_filename,
x_label='Time [min]',
y_label='Polymer Fraction') as axes:
contexts.plot(axes, 'plot', ftimes, fdata, 'k.')
contexts.plot(axes, 'plot', ptimes, pdata, 'r.')
contexts.plot(axes, 'plot', stimes, slengths, 'k-')
contexts.plot(axes, 'plot', stimes, sadppi, 'r-')
axes.set_xlim(0, 35)
def copoly_halftime_plot(filename):
adp_halftimes = data.load_data('results/adp_copoly_halftimes.dat')
nh_halftimes = data.load_data('results/nh_copoly_halftimes.dat')
adp_v_halftimes = data.load_data(
'results/adp_copoly_halftimes_vectorial.dat')
nh_v_halftimes = data.load_data(
'results/nh_copoly_halftimes_vectorial.dat')
fractions, combined_data = _combine_data(adp_halftimes, nh_halftimes)
vfractions, vcombined_data = _combine_data(adp_v_halftimes, nh_v_halftimes)
with contexts.basic_figure(filename,
y_label=r'Halftime [s]',
logscale_y=True) as axes:
for local_data, lt, in zip(combined_data, LINETYPES):
contexts.plot(axes, 'plot', fractions, local_data, lt)
for vld in vcombined_data:
contexts.plot(axes, 'plot', vfractions, vld, 'k-')
# new_x_tick_labels = [10, 5, 0, 5, 10]
#
# axes.set_xticks([-10, -5, 0, 5, 10])
new_x_tick_labels = [50, 40, 30, 20, 10, 0, 10]
axes.set_xticks([-50, -40, -30, -20, -10, 0, 10])
axes.set_xticklabels(new_x_tick_labels)
axes.set_ylim(10, 10**5)
axes.text(X_LABEL_PADDING,
X_LABEL_MARGIN,
'ADP-actin [%]',
verticalalignment='top',
horizontalalignment='left',
transform=axes.transAxes)
axes.text(1 - X_LABEL_PADDING,
X_LABEL_MARGIN,
'NH-actin [%]',
verticalalignment='top',
horizontalalignment='right',
transform=axes.transAxes)
# \rho_d arrows
axes.annotate(INCREASING_RHO_TEXT,
xy=(-HT_ARROW_X_OFFSET, TIMECOURSE_HALFTIME),
xytext=(-HT_ARROW_X_OFFSET, 6e3),
arrowprops={
'facecolor': 'black',
'arrowstyle': '->'
},
horizontalalignment='center',
verticalalignment='top',
size=settings.SMALL_FONT_SIZE)
axes.annotate(INCREASING_RHO_TEXT,
xy=(HT_ARROW_X_OFFSET, TIMECOURSE_HALFTIME),
xytext=(HT_ARROW_X_OFFSET, 17),
arrowprops={
'facecolor': 'black',
'arrowstyle': '->'
},
horizontalalignment='center',
verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
axes.axvline(0, 0, 1, linestyle=':', linewidth=0.5, color='k')
def constraint_plot():
MELKI_THRESHOLD = 4.5
FNC_THRESHOLD = 7.5
DEPOLY_THRESHOLD = 3
melki_constraints = data.load_data('results/melki_cooperative_fit.dat')
fnc_constraints = data.load_data('results/fnc_cooperative_qof.dat')
depoly_constraints = data.load_data('results/depoly_cooperative_qof.dat')
mrho, mchi = melki_constraints[0], melki_constraints[5]
frho, fchi = fnc_constraints[0], fnc_constraints[1]
drho, dchi = depoly_constraints[0], depoly_constraints[1]
mrho = numpy.array(mrho)
mchi = numpy.array(mchi) / MELKI_THRESHOLD
frho = numpy.array(frho)
fchi = numpy.array(fchi) / FNC_THRESHOLD
drho = numpy.array(drho)
dchi = numpy.array(dchi) / DEPOLY_THRESHOLD
lx = numpy.linspace(0, 10, 101)
lmr = numpy.log10(mrho)
lfr = numpy.log10(frho)
ldr = numpy.log10(drho)
m_inter = my_spline(lmr, mchi, lx)
f_inter = my_spline(lfr, fchi, lx)
d_inter = my_spline(ldr, dchi, lx)
# m_inter = scipy.interpolate.UnivariateSpline(lmr, mchi, k=3)(lx)
# f_inter = scipy.interpolate.UnivariateSpline(lfr, fchi, k=3)(lx)
# d_inter = scipy.interpolate.UnivariateSpline(ldr, dchi, k=3)(lx)
# m_inter = scipy.interpolate.InterpolatedUnivariateSpline(lmr, mchi, k=4)(lx)
# f_inter = scipy.interpolate.InterpolatedUnivariateSpline(lfr, fchi, k=4)(lx)
# d_inter = scipy.interpolate.InterpolatedUnivariateSpline(ldr, dchi, k=4)(lx)
# m_inter = numpy.exp(scipy.interpolate.InterpolatedUnivariateSpline(lmr,
# numpy.log(mchi), k=4)(lx))
# f_inter = numpy.exp(scipy.interpolate.InterpolatedUnivariateSpline(lfr,
# numpy.log(fchi), k=4)(lx))
# d_inter = numpy.exp(scipy.interpolate.InterpolatedUnivariateSpline(ldr,
# numpy.log(dchi), k=4)(lx))
with contexts.basic_figure('plots/cooperativity_constraints.pdf',
x_label=r'$\rho_d$',
y_label=r'Scaled Quality of Fit [AU]',
logscale_x=False) as axes:
# pylab.ioff()
# figure = pylab.figure()
# axes = pylab.gca()
axes.fill_between(
lx,
m_inter,
1,
where=m_inter <= 1,
color='r',
alpha=0.6,
# color='#BB6666',
interpolate=True)
axes.fill_between(
lx,
f_inter,
1,
where=f_inter <= 1,
color='b',
alpha=0.6,
# color='#6666BB',
interpolate=True)
axes.fill_between(
lx,
d_inter,
1,
where=d_inter <= 1,
color='y',
alpha=0.6,
# color='#6666BB',
interpolate=True)
axes.plot(lx, m_inter, 'k-')
axes.plot(lx, f_inter, 'k--')
axes.plot(lx, d_inter, 'k-.')
axes.axhline(1, 0, 1, color='k')
# XXX Optional vertical line
# axes.axvline(0, 0, 1.0/6, color='k')
axes.set_xlim([-1, 11])
axes.set_xticks([0, 2, 4, 6, 8, 10])
axes.set_xticklabels(
[1, r'$10^2$', r'$10^4$', r'$10^6$', r'$10^8$', r'$10^{10}$'])
axes.set_yticks([0, 1, 2, 3])
axes.set_ylim([0, 3])
def constraint_plot():
MELKI_THRESHOLD = 4.5
FNC_THRESHOLD = 7.5
DEPOLY_THRESHOLD = 3
melki_constraints = data.load_data('results/melki_cooperative_fit.dat')
fnc_constraints = data.load_data('results/fnc_cooperative_qof.dat')
depoly_constraints = data.load_data('results/depoly_cooperative_qof.dat')
mrho, mchi = melki_constraints[0], melki_constraints[5]
frho, fchi = fnc_constraints[0], fnc_constraints[1]
drho, dchi = depoly_constraints[0], depoly_constraints[1]
mrho = numpy.array(mrho)
mchi = numpy.array(mchi) / MELKI_THRESHOLD
frho = numpy.array(frho)
fchi = numpy.array(fchi) / FNC_THRESHOLD
drho = numpy.array(drho)
dchi = numpy.array(dchi) / DEPOLY_THRESHOLD
lx = numpy.linspace(0, 10, 101)
lmr = numpy.log10(mrho)
lfr = numpy.log10(frho)
ldr = numpy.log10(drho)
m_inter = my_spline(lmr, mchi, lx)
f_inter = my_spline(lfr, fchi, lx)
d_inter = my_spline(ldr, dchi, lx)
# m_inter = scipy.interpolate.UnivariateSpline(lmr, mchi, k=3)(lx)
# f_inter = scipy.interpolate.UnivariateSpline(lfr, fchi, k=3)(lx)
# d_inter = scipy.interpolate.UnivariateSpline(ldr, dchi, k=3)(lx)
# m_inter = scipy.interpolate.InterpolatedUnivariateSpline(lmr, mchi, k=4)(lx)
# f_inter = scipy.interpolate.InterpolatedUnivariateSpline(lfr, fchi, k=4)(lx)
# d_inter = scipy.interpolate.InterpolatedUnivariateSpline(ldr, dchi, k=4)(lx)
# m_inter = numpy.exp(scipy.interpolate.InterpolatedUnivariateSpline(lmr,
# numpy.log(mchi), k=4)(lx))
# f_inter = numpy.exp(scipy.interpolate.InterpolatedUnivariateSpline(lfr,
# numpy.log(fchi), k=4)(lx))
# d_inter = numpy.exp(scipy.interpolate.InterpolatedUnivariateSpline(ldr,
# numpy.log(dchi), k=4)(lx))
with contexts.basic_figure('plots/cooperativity_constraints.pdf',
x_label=r'$\rho_d$',
y_label=r'Scaled Quality of Fit [AU]',
logscale_x=False) as axes:
# pylab.ioff()
# figure = pylab.figure()
# axes = pylab.gca()
axes.fill_between(lx, m_inter, 1, where=m_inter <= 1,
color='r', alpha=0.6,
# color='#BB6666',
interpolate=True)
axes.fill_between(lx, f_inter, 1, where=f_inter <= 1,
color='b', alpha=0.6,
# color='#6666BB',
interpolate=True)
axes.fill_between(lx, d_inter, 1, where=d_inter <= 1,
color='y', alpha=0.6,
# color='#6666BB',
interpolate=True)
axes.plot(lx, m_inter, 'k-')
axes.plot(lx, f_inter, 'k--')
axes.plot(lx, d_inter, 'k-.')
axes.axhline(1, 0, 1, color='k')
# XXX Optional vertical line
# axes.axvline(0, 0, 1.0/6, color='k')
axes.set_xlim([-1, 11])
axes.set_xticks([0, 2, 4, 6, 8, 10])
axes.set_xticklabels([1, r'$10^2$', r'$10^4$', r'$10^6$',
r'$10^8$', r'$10^{10}$'])
axes.set_yticks([0, 1, 2, 3])
axes.set_ylim([0, 3])
