def asymptotic_plot(figure):
results = data.load_data('results/asymptotic_adppi_v_pi.dat')
pis, fractions = results[0], results[1:]
with contexts.subplot(figure, (2, 2, 2), title='C',
x_label=r'Pi Concentration [$\mu$M]',
y_label=r'Asymptotic Fraction [F-ADP-Pi-actin]',
logscale_x=True) as axes:
# for frac in fractions:
# contexts.plot(axes, 'plot', pis, frac)
contexts.plot(axes, 'plot', pis, fractions[0], 'b')
axes.set_ylim(0, 1)
axes.axhline(0.5, 0, 1, linestyle='-', linewidth=0.5, color='k')
axes.axvline(HALF_CONCENTRATION, 0, 0.5,
linestyle=':', linewidth=0.5, color='k')
axes.annotate(r'$c^*$', xy=(HALF_CONCENTRATION, 0.001),
xytext=(HALF_CONCENTRATION + 2000, 0.15),
arrowprops={'facecolor': 'black',
'arrowstyle': '->'},
size=settings.SMALL_FONT_SIZE)
axes.text(4000, 0.8, r'$\rho_d =\,1$',
horizontalalignment='right', # verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
def copoly_timecourse_plot(figure, xmax=1000, ymax=35):
timecourses = data.load_data('results/copoly_timecourse.dat')
with contexts.subplot(figure, (2, 1, 1), title='A',
x_label='Time [s]',
y_label=r'Concentrations [$\mu$M]') as axes:
# factin
contexts.plot(axes, 'plot', timecourses[0], timecourses[1], 'b-.')
# pi
contexts.plot(axes, 'plot', timecourses[0], timecourses[3], 'g-')
axes.set_xlim(0, xmax)
axes.set_ylim(0, ymax)
# Lines to highlight halftime
axes.axhline(15, 0, 1, linestyle='-', linewidth=0.5, color='k')
axes.axvline(TIMECOURSE_HALFTIME, 0, 15.0 / 35,
linestyle=':', linewidth=0.5, color='k')
# Halftime label with arrow
# axes.annotate('halftime', xy=(TIMECOURSE_HALFTIME, 0.05),
axes.annotate(r'$t_{\frac{1}{2}}$', xy=(TIMECOURSE_HALFTIME, 0.05),
xytext=(TIMECOURSE_HALFTIME + 200, 5),
arrowprops={'facecolor': 'black',
'arrowstyle': '->'},
size=settings.SMALL_FONT_SIZE)
# Curve labels
axes.text(0.5 * xmax, 30.5, '[F-actin]',
horizontalalignment='center', verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
axes.text(600, 20.5, '[Pi]',
horizontalalignment='right', verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
def copoly_adp_only(figure):
adp_halftimes = data.load_data('results/adp_copoly_halftimes.dat')
adp_v_halftimes = data.load_data(
'results/adp_copoly_halftimes_vectorial.dat')
fractions, halftimes = adp_halftimes[0], adp_halftimes[1:]
v_fractions, v_halftimes = adp_v_halftimes[0], adp_v_halftimes[1]
with contexts.subplot(
figure,
(1, 1, 1), #title='B',
y_label=r'[Pi] Halftime [s]',
x_label=r'% ADP-actin') as axes:
for ht, lt, in zip(halftimes, LINETYPES):
# contexts.plot(axes, 'plot', fractions, numpy.array(ht)/ht[0], lt)
contexts.plot(axes, 'plot', fractions, numpy.array(ht), lt)
# contexts.plot(axes, 'plot', v_fractions, numpy.array(v_halftimes)/v_halftimes[0], 'k-')
contexts.plot(axes, 'plot', v_fractions, numpy.array(v_halftimes),
'k-')
new_x_tick_labels = [0, 5, 10, 15, 20]
axes.set_xticks([0, 0.05, 0.1, 0.15, 0.2])
axes.set_xticklabels(new_x_tick_labels)
def melki_rate_error_plot(figure):
# results = data.load_data('results/melki_rate_sensitivities.dat')
results = data.load_data("results/melki_cooperative_fit.dat")
cooperativities, rates, min_rates, max_rates = results[:4]
theoretical_rates = 1.0 / (HALFTIME * numpy.sqrt(cooperativities))
errors = numpy.array(numpy.array(rates) - numpy.array(min_rates)) / theoretical_rates
rates = numpy.array(rates) / theoretical_rates
# with contexts.basic_figure('plots/melki_rate_errors.pdf',
with contexts.subplot(
figure,
(2, 1, 2),
title="B",
x_label=r"Pi Dissociation Cooperativity, $\rho_d$",
y_label=r"$r_d$ [Simulation / Theory]",
logscale_x=True,
# logscale_y=True
) as axes:
axes.axvline(RHO_CRIT, 0, 1, linestyle="--", color="g", linewidth=0.5)
contexts.plot(axes, "errorbar", cooperativities, rates, errors, fmt="k.")
contexts.plot(axes, "plot", cooperativities, [1 for c in cooperativities], "r-", linewidth=0.5)
axes.set_xlim([0.1, 10 ** 12])
axes.set_xticks([1, 100, 10000, 1000000, 100000000, 10000000000])
axes.set_ylim([0, 1.2])
def melki_rate_error_plot(figure):
# results = data.load_data('results/melki_rate_sensitivities.dat')
results = data.load_data('results/melki_cooperative_fit.dat')
cooperativities, rates, min_rates, max_rates = results[:4]
theoretical_rates = 1.0 / (HALFTIME * numpy.sqrt(cooperativities))
errors = numpy.array(numpy.array(rates) - numpy.array(min_rates)) / theoretical_rates
rates = numpy.array(rates) / theoretical_rates
# with contexts.basic_figure('plots/melki_rate_errors.pdf',
with contexts.subplot(figure, (2, 1, 2), title='B',
x_label=r'Pi Dissociation Cooperativity, $\rho_d$',
y_label=r'$r_d$ [Simulation / Theory]',
logscale_x=True,
# logscale_y=True
) as axes:
axes.axvline(RHO_CRIT, 0, 1, linestyle='--', color='g', linewidth=0.5)
contexts.plot(axes, 'errorbar', cooperativities,
rates, errors, fmt='k.')
contexts.plot(axes, 'plot', cooperativities,
[1 for c in cooperativities], 'r-', linewidth=0.5)
axes.set_xlim([0.1, 10**12])
axes.set_xticks([1, 100, 10000, 1000000,
100000000, 10000000000])
axes.set_ylim([0, 1.2])
def tip_filaments():
random_results = data.load_data('results/depolymerization_timecourses.dat')
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
rtimes, rvalues = numpy.array(random_results[0]), random_results[1:]
etimes, evals = numpy.array(expt[0]), expt[1]
rtimes -= 300
# with contexts.basic_figure('plots/depoly_tip_filaments.pdf',
with contexts.subplot(figure, (2, 2, 3),
title='A',
x_label='Time [s]',
y_label=r'Filament Length [$\mu$m]') as axes:
for y in rvalues:
y = numpy.array(y)
y *= 0.0027
contexts.plot(axes,
'plot',
rtimes,
y,
'-',
color='#FFA0A0',
linewidth=THIN_LINE)
contexts.plot(axes, 'plot', etimes, evals, 'k', linewidth=0.7)
axes.set_ylim(0, 15)
axes.set_xlim(0, 1000)
def tip_filaments():
random_results = data.load_data('results/depolymerization_timecourses.dat')
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
rtimes, rvalues = numpy.array(random_results[0]), random_results[1:]
etimes, evals = numpy.array(expt[0]), expt[1]
rtimes -= 300
# with contexts.basic_figure('plots/depoly_tip_filaments.pdf',
with contexts.subplot(figure, (2, 2, 3), title='A',
x_label='Time [s]',
y_label=r'Filament Length [$\mu$m]') as axes:
for y in rvalues:
y = numpy.array(y)
y *= 0.0027
contexts.plot(axes, 'plot', rtimes, y, '-', color='#FFA0A0',
linewidth=THIN_LINE)
contexts.plot(axes, 'plot', etimes, evals, 'k', linewidth=0.7)
axes.set_ylim(0, 15)
axes.set_xlim(0, 1000)
def melki_rate_fit_quality(figure):
results = data.load_data('results/melki_cooperative_fit.dat')
(cooperativities, rates, min_rates, max_rates, rate_pe,
chi2, min_chi2, max_chi2, chi2_pe) = results
v_results = data.load_data('results/melki_vectorial_fit.dat')
(v_rate, junk, junk, junk,
v_chi2, v_min_chi2, v_max_chi2, junk) = zip(*v_results)[0]
with contexts.subplot(figure, (2, 1, 2), title='B',
x_label=r'$\rho_d$',
y_label=r'$\chi^2$ Comparison To Data',
logscale_x=True) as axes:
axes.fill_between([0.1, 1.0e12],
[v_min_chi2, v_min_chi2],
v_max_chi2, color='#CCCCFF')
axes.axhline(v_chi2, 0, 1, linestyle=':', color='b')
axes.axvline(RHO_CRIT, 0, 1, linestyle='--', color='g', linewidth=0.5)
contexts.plot(axes, 'errorbar', cooperativities,
chi2, [c - m for c, m in zip(chi2, min_chi2)],
fmt='k.')
axes.set_xlim([0.1, 10**12])
axes.set_ylim([0, 14])
axes.set_xticks([1, 100, 10000, 1000000,
100000000, 10000000000])
def cooperativity_plot(figure):
x, y = data.load_data('results/rho_v_pi.dat')
with contexts.subplot(figure, (2, 2, 4), title='D',
# x_label=r'Asymptotic Fraction [F-ADP-Pi-actin]',
x_label=r'[Pi]$_{\frac{1}{2}}$ [$\mu$M]',
y_label=r'Pi Dissociation Cooperativity, $\rho_d$',
logscale_x=True, logscale_y=True) as axes:
contexts.plot(axes, 'plot', x, y, 'k')
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 cooperativity_plot(figure):
x, y = data.load_data('results/rho_v_pi.dat')
with contexts.subplot(
figure,
(2, 2, 4),
title='D',
# x_label=r'Asymptotic Fraction [F-ADP-Pi-actin]',
x_label=r'[Pi]$_{\frac{1}{2}}$ [$\mu$M]',
y_label=r'Pi Dissociation Cooperativity, $\rho_d$',
logscale_x=True,
logscale_y=True) as axes:
contexts.plot(axes, 'plot', x, y, 'k')
def halftime_plot(figure, data_filename='results/halftime_v_rho.dat'):
results = data.load_data(data_filename)
pis, halftimes = results[0], results[1:]
pis = numpy.array(pis) / 1000.0
# with contexts.subplot(figure, (2, 2, 3), title='B',
with contexts.subplot(figure, (1, 1, 1), #title='B',
x_label=r'Pi Concentration [mM]',
y_label=r'[Pi] Halftime [s]',
logscale_x=True, logscale_y=False) as axes:
for ht in halftimes:
contexts.plot(axes, 'plot', pis, ht)
axes.set_xlim(0, 1000)
axes.set_ylim(0, 3000)
def copoly_timecourse_plot(figure, xmax=1000, ymax=35):
timecourses = data.load_data('results/copoly_timecourse.dat')
with contexts.subplot(figure, (2, 1, 1),
title='A',
x_label='Time [s]',
y_label=r'Concentrations [$\mu$M]') as axes:
# factin
contexts.plot(axes, 'plot', timecourses[0], timecourses[1], 'b-.')
# pi
contexts.plot(axes, 'plot', timecourses[0], timecourses[3], 'g-')
axes.set_xlim(0, xmax)
axes.set_ylim(0, ymax)
# Lines to highlight halftime
axes.axhline(15, 0, 1, linestyle='-', linewidth=0.5, color='k')
axes.axvline(TIMECOURSE_HALFTIME,
0,
15.0 / 35,
linestyle=':',
linewidth=0.5,
color='k')
# Halftime label with arrow
# axes.annotate('halftime', xy=(TIMECOURSE_HALFTIME, 0.05),
axes.annotate(r'$t_{\frac{1}{2}}$',
xy=(TIMECOURSE_HALFTIME, 0.05),
xytext=(TIMECOURSE_HALFTIME + 200, 5),
arrowprops={
'facecolor': 'black',
'arrowstyle': '->'
},
size=settings.SMALL_FONT_SIZE)
# Curve labels
axes.text(0.5 * xmax,
30.5,
'[F-actin]',
horizontalalignment='center',
verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
axes.text(600,
20.5,
'[Pi]',
horizontalalignment='right',
verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
def cc_tip(figure):
results = data.load_data('results/cc_d_tip.dat')
pars, ccs, ds = results
with contexts.subplot(figure, (2, 1, 1), title='A',
y_label=r'Critical Concentration [$\mu$M]',
logscale_x=False, logscale_y=False) as axes:
contexts.plot(axes, 'plot', pars, ccs, 'k-')
with contexts.subplot(figure, (2, 1, 2), title='B',
y_label=r'Diffusion Coefficient [mon^2/s]',
x_label=r'Barbed End Pi Dissociation Rate [s$^{-1}$]',
logscale_x=False, logscale_y=False) as axes:
contexts.plot(axes, 'plot', pars, ds, 'k-')
def halftime_plot(figure, data_filename='results/halftime_v_rho.dat'):
results = data.load_data(data_filename)
pis, halftimes = results[0], results[1:]
pis = numpy.array(pis) / 1000.0
# with contexts.subplot(figure, (2, 2, 3), title='B',
with contexts.subplot(
figure,
(1, 1, 1), #title='B',
x_label=r'Pi Concentration [mM]',
y_label=r'[Pi] Halftime [s]',
logscale_x=True,
logscale_y=False) as axes:
for ht in halftimes:
contexts.plot(axes, 'plot', pis, ht)
axes.set_xlim(0, 1000)
axes.set_ylim(0, 3000)
def melki_rate_fit_quality(figure):
results = data.load_data("results/melki_cooperative_fit.dat")
(cooperativities, rates, min_rates, max_rates, rate_pe, chi2, min_chi2, max_chi2, chi2_pe) = results
v_results = data.load_data("results/melki_vectorial_fit.dat")
(v_rate, junk, junk, junk, v_chi2, v_min_chi2, v_max_chi2, junk) = zip(*v_results)[0]
with contexts.subplot(
figure, (2, 1, 2), title="B", x_label=r"$\rho_d$", y_label=r"$\chi^2$ Comparison To Data", logscale_x=True
) as axes:
axes.fill_between([0.1, 1.0e12], [v_min_chi2, v_min_chi2], v_max_chi2, color="#CCCCFF")
axes.axhline(v_chi2, 0, 1, linestyle=":", color="b")
axes.axvline(RHO_CRIT, 0, 1, linestyle="--", color="g", linewidth=0.5)
contexts.plot(axes, "errorbar", cooperativities, chi2, [c - m for c, m in zip(chi2, min_chi2)], fmt="k.")
axes.set_xlim([0.1, 10 ** 12])
axes.set_ylim([0, 14])
axes.set_xticks([1, 100, 10000, 1000000, 100000000, 10000000000])
def cc_tip(figure):
results = data.load_data('results/cc_d_tip.dat')
pars, ccs, ds = results
with contexts.subplot(figure, (2, 1, 1),
title='A',
y_label=r'Critical Concentration [$\mu$M]',
logscale_x=False,
logscale_y=False) as axes:
contexts.plot(axes, 'plot', pars, ccs, 'k-')
with contexts.subplot(
figure, (2, 1, 2),
title='B',
y_label=r'Diffusion Coefficient [mon^2/s]',
x_label=r'Barbed End Pi Dissociation Rate [s$^{-1}$]',
logscale_x=False,
logscale_y=False) as axes:
contexts.plot(axes, 'plot', pars, ds, 'k-')
def crit_conc(figure):
results = data.load_data('results/cc_d_cooperative.dat')
v_results = data.load_data('results/cc_d_vectorial.dat')
coops, ccs, ds = results
vcc, vd = v_results[0][0], v_results[1][0]
with contexts.subplot(figure, (2, 1, 1), title='A',
y_label=r'Critical Concentration [$\mu$M]',
logscale_x=True, logscale_y=False) as axes:
contexts.plot(axes, 'plot', coops, ccs, 'k-')
axes.axhline(vcc, 0, 1, linestyle=':', linewidth=0.5, color='k')
axes.set_ylim(0.1, 0.2)
with contexts.subplot(figure, (2, 1, 2), title='B',
y_label=r'Diffusion Coefficient [mon^2/s]',
logscale_x=True, logscale_y=False) as axes:
contexts.plot(axes, 'plot', coops, ds, 'k-')
axes.axhline(vd, 0, 1, linestyle=':', linewidth=0.5, color='k')
def inset_error_plot(figure):
results = data.load_data('results/melki_cooperative_fit.dat')
cooperativities, rates, min_rates, max_rates = results[:4]
theoretical_rates = 1.0 / (HALFTIME * numpy.sqrt(cooperativities))
errors = numpy.array(numpy.array(rates) - numpy.array(min_rates)) / theoretical_rates
rates = numpy.array(rates) / theoretical_rates
# Box: left, bottom, width, height
box = (0.395, 0.25, 0.22, 0.22)
axes = figure.add_axes(box)
axes.set_xscale('log')
axes.set_xlabel(r'$\rho_d$', size=6, labelpad=0)
axes.set_ylabel(r'$r_d^{sim} / r_d^{th}$', size=6, labelpad=-1)
for spine in axes.spines.values():
spine.set_linewidth(0.5)
axes.axvline(RHO_CRIT, 0, 1, linestyle='--', color='g', linewidth=0.3,
dashes=(2, 2))
contexts.plot(axes, 'errorbar', cooperativities,
rates, errors, fmt='k.', markersize=4, linewidth=0.5, capsize=2)
contexts.plot(axes, 'plot', cooperativities,
[1 for c in cooperativities], 'r-', linewidth=0.3)
axes.set_xlim([0.01, 10**12])
axes.set_xticks([1, 100000, 10000000000])
axes.minorticks_off()
axes.xaxis.set_tick_params(size=2, pad=2)
axes.yaxis.set_tick_params(size=2, pad=2)
axes.set_ylim([0, 1.2])
axes.set_yticks([0, 1])
for label in itertools.chain(axes.get_xticklabels(),
axes.get_yticklabels()):
label.set_size(4)
def copoly_adp_only(figure):
adp_halftimes = data.load_data('results/adp_copoly_halftimes.dat')
adp_v_halftimes = data.load_data('results/adp_copoly_halftimes_vectorial.dat')
fractions, halftimes = adp_halftimes[0], adp_halftimes[1:]
v_fractions, v_halftimes = adp_v_halftimes[0], adp_v_halftimes[1]
with contexts.subplot(figure, (1, 1, 1), #title='B',
y_label=r'[Pi] Halftime [s]',
x_label=r'% ADP-actin') as axes:
for ht, lt, in zip(halftimes, LINETYPES):
# contexts.plot(axes, 'plot', fractions, numpy.array(ht)/ht[0], lt)
contexts.plot(axes, 'plot', fractions, numpy.array(ht), lt)
# contexts.plot(axes, 'plot', v_fractions, numpy.array(v_halftimes)/v_halftimes[0], 'k-')
contexts.plot(axes, 'plot', v_fractions, numpy.array(v_halftimes), 'k-')
new_x_tick_labels = [0, 5, 10, 15, 20]
axes.set_xticks([0, 0.05, 0.1, 0.15, 0.2])
axes.set_xticklabels(new_x_tick_labels)
def tip_fit(figure):
best_tc = data.load_data('results/depoly_timecourse.dat')
times, values = numpy.array(best_tc[0]), numpy.array(best_tc[1])
times -= 300
values *= 0.0027
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
etimes, evals = numpy.array(expt[0]), expt[1]
# with contexts.basic_figure('plots/depoly_tip_fit_timecourse.pdf',
with contexts.subplot(figure, (2, 2, 2), title='B'
# x_label='Time [s]',
# y_label=r'Filament Length [$\mu$m]'
) as axes:
contexts.plot(axes, 'plot', times, values, 'r-')
contexts.plot(axes, 'plot', etimes, evals, 'k-')
axes.set_xlim(0, 1000)
axes.set_ylim(0, 15)
def asymptotic_plot(figure):
results = data.load_data('results/asymptotic_adppi_v_pi.dat')
pis, fractions = results[0], results[1:]
with contexts.subplot(figure, (2, 2, 2),
title='C',
x_label=r'Pi Concentration [$\mu$M]',
y_label=r'Asymptotic Fraction [F-ADP-Pi-actin]',
logscale_x=True) as axes:
# for frac in fractions:
# contexts.plot(axes, 'plot', pis, frac)
contexts.plot(axes, 'plot', pis, fractions[0], 'b')
axes.set_ylim(0, 1)
axes.axhline(0.5, 0, 1, linestyle='-', linewidth=0.5, color='k')
axes.axvline(HALF_CONCENTRATION,
0,
0.5,
linestyle=':',
linewidth=0.5,
color='k')
axes.annotate(r'$c^*$',
xy=(HALF_CONCENTRATION, 0.001),
xytext=(HALF_CONCENTRATION + 2000, 0.15),
arrowprops={
'facecolor': 'black',
'arrowstyle': '->'
},
size=settings.SMALL_FONT_SIZE)
axes.text(
4000,
0.8,
r'$\rho_d =\,1$',
horizontalalignment='right', # verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
def tip_fit(figure):
best_tc = data.load_data('results/depoly_timecourse.dat')
times, values = numpy.array(best_tc[0]), numpy.array(best_tc[1])
times -= 300
values *= 0.0027
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
etimes, evals = numpy.array(expt[0]), expt[1]
# with contexts.basic_figure('plots/depoly_tip_fit_timecourse.pdf',
with contexts.subplot(
figure, (2, 2, 2),
title='B'
# x_label='Time [s]',
# y_label=r'Filament Length [$\mu$m]'
) as axes:
contexts.plot(axes, 'plot', times, values, 'r-')
contexts.plot(axes, 'plot', etimes, evals, 'k-')
axes.set_xlim(0, 1000)
axes.set_ylim(0, 15)
def crit_conc(figure):
results = data.load_data('results/cc_d_cooperative.dat')
v_results = data.load_data('results/cc_d_vectorial.dat')
coops, ccs, ds = results
vcc, vd = v_results[0][0], v_results[1][0]
with contexts.subplot(figure, (2, 1, 1),
title='A',
y_label=r'Critical Concentration [$\mu$M]',
logscale_x=True,
logscale_y=False) as axes:
contexts.plot(axes, 'plot', coops, ccs, 'k-')
axes.axhline(vcc, 0, 1, linestyle=':', linewidth=0.5, color='k')
axes.set_ylim(0.1, 0.2)
with contexts.subplot(figure, (2, 1, 2),
title='B',
y_label=r'Diffusion Coefficient [mon^2/s]',
logscale_x=True,
logscale_y=False) as axes:
contexts.plot(axes, 'plot', coops, ds, 'k-')
axes.axhline(vd, 0, 1, linestyle=':', linewidth=0.5, color='k')
def inset_error_plot(figure):
results = data.load_data("results/melki_cooperative_fit.dat")
cooperativities, rates, min_rates, max_rates = results[:4]
theoretical_rates = 1.0 / (HALFTIME * numpy.sqrt(cooperativities))
errors = numpy.array(numpy.array(rates) - numpy.array(min_rates)) / theoretical_rates
rates = numpy.array(rates) / theoretical_rates
# Box: left, bottom, width, height
box = (0.395, 0.25, 0.22, 0.22)
axes = figure.add_axes(box)
axes.set_xscale("log")
axes.set_xlabel(r"$\rho_d$", size=6, labelpad=0)
axes.set_ylabel(r"$r_d^{sim} / r_d^{th}$", size=6, labelpad=-1)
for spine in axes.spines.values():
spine.set_linewidth(0.5)
axes.axvline(RHO_CRIT, 0, 1, linestyle="--", color="g", linewidth=0.3, dashes=(2, 2))
contexts.plot(axes, "errorbar", cooperativities, rates, errors, fmt="k.", markersize=4, linewidth=0.5, capsize=2)
contexts.plot(axes, "plot", cooperativities, [1 for c in cooperativities], "r-", linewidth=0.3)
axes.set_xlim([0.01, 10 ** 12])
axes.set_xticks([1, 100000, 10000000000])
axes.minorticks_off()
axes.xaxis.set_tick_params(size=2, pad=2)
axes.yaxis.set_tick_params(size=2, pad=2)
axes.set_ylim([0, 1.2])
axes.set_yticks([0, 1])
for label in itertools.chain(axes.get_xticklabels(), axes.get_yticklabels()):
label.set_size(4)
def non_be_rates(figure):
results = data.load_data('results/melki_cooperative_fit.dat')
nb_results = data.load_data('results/melki_non_be_coop_fit.dat')
nop_results = data.load_data('results/melki_no_pointed_coop_fit.dat')
cooperativities, rates, min_rates, max_rates = results[:4]
nbc, nbr, nb_minr, nb_maxr = nb_results[:4]
nopc, nopr, nopminr, nopmaxr = nop_results[:4]
theoretical_rates = 1.0 / (HALFTIME * numpy.sqrt(cooperativities))
errors = numpy.array(numpy.array(rates) - numpy.array(min_rates)) / theoretical_rates
rates = numpy.array(rates) / theoretical_rates
nberrors = numpy.array(numpy.array(nbr) - numpy.array(nb_minr)) / theoretical_rates
nbrates = numpy.array(nbr) / theoretical_rates
noperrors = numpy.array(numpy.array(nopr) - numpy.array(nopminr)) / theoretical_rates
noprates = numpy.array(nopr) / theoretical_rates
with contexts.subplot(figure, (2, 1, 1), title='A',
# x_label=r'Pi Dissociation Cooperativity, $\rho_d$',
y_label=r'$r_d$ [Simulation / Theory]',
logscale_x=True,
) as axes:
axes.axvline(RHO_CRIT, 0, 1, linestyle='--', color='g', linewidth=0.5)
# With enhanced BE dissociation rate
contexts.plot(axes, 'errorbar', cooperativities,
rates, errors, fmt='k.')
# Without enhanced BE dissociation rate
contexts.plot(axes, 'errorbar', cooperativities,
nbrates, nberrors, fmt='b.')
# No pointed end kinetics
contexts.plot(axes, 'errorbar', cooperativities,
noprates, noperrors, fmt='g.')
contexts.plot(axes, 'plot', cooperativities,
[1 for c in cooperativities], 'r-', linewidth=0.5)
axes.set_xlim([0.1, 10**12])
axes.set_xticks([1, 100, 10000, 1000000,
100000000, 10000000000])
axes.set_ylim([0, 1.2])
def non_be_quality(figure):
results = data.load_data('results/melki_cooperative_fit.dat')
(cooperativities, rates, min_rates, max_rates, rate_pe, chi2, min_chi2,
max_chi2, chi2_pe) = results
nb_results = data.load_data('results/melki_non_be_coop_fit.dat')
(nbc, nbr, nbminr, nbmaxr, nbrpe, nbchi2, nbminc2, nbmaxc2,
nbcpe) = nb_results
nop_results = data.load_data('results/melki_no_pointed_coop_fit.dat')
(nbc, nopr, junk, junk, jukn, nopc2, nopminc2, nopmaxc2,
junk) = nop_results
v_results = data.load_data('results/melki_vectorial_fit.dat')
(v_rate, junk, junk, junk, v_chi2, v_min_chi2, v_max_chi2,
junk) = zip(*v_results)[0]
v_nb_results = data.load_data('results/melki_non_be_vec_fit.dat')
(v_nbr, v_nbminr, v_nbmaxr, v_nbrpe, v_nbchi2, v_nbminc2, v_nbmaxc2,
v_nbcpe) = zip(*v_nb_results)[0]
with contexts.subplot(figure, (2, 1, 2),
title='B',
x_label=r'Pi Dissociation Cooperativity, $\rho_d$',
y_label=r'$\chi^2$ Comparison To Data',
logscale_x=True) as axes:
axes.fill_between([0.1, 1.0e12], [v_min_chi2, v_min_chi2],
v_max_chi2,
color='#AAAAAA')
axes.fill_between([0.1, 1.0e12], [v_nbminc2, v_nbminc2],
v_nbmaxc2,
color='#CCCCFF')
axes.axhline(v_chi2, 0, 1, linestyle=':', color='k')
axes.axhline(v_nbchi2, 0, 1, linestyle=':', color='b')
axes.axvline(RHO_CRIT, 0, 1, linestyle='--', color='g', linewidth=0.5)
contexts.plot(axes,
'errorbar',
cooperativities,
chi2, [c - m for c, m in zip(chi2, min_chi2)],
fmt='k.')
contexts.plot(axes,
'errorbar',
cooperativities,
nbchi2, [c - m for c, m in zip(nbchi2, nbminc2)],
fmt='b.')
contexts.plot(axes,
'errorbar',
cooperativities,
nopc2, [c - m for c, m in zip(nopc2, nopminc2)],
fmt='g.')
axes.set_xlim([0.1, 10**12])
axes.set_ylim([0, 14])
axes.set_xticks([1, 100, 10000, 1000000, 100000000, 10000000000])
def melki_rate_plot(figure):
# results = data.load_data('results/melki_rates.dat')
results = data.load_data('results/melki_cooperative_fit.dat')
v_results = data.load_data('results/melki_vectorial_fit.dat')
# results = data.load_data('results/melki_rate_sensitivities.dat')
# cooperativities, rates, statistical_errors, halftimes, hte = results
# cooperativities, rates, errors = results[:3]
cooperativities, rates = results[:2]
v_rate_pack = zip(*v_results)[0][:3]
# with contexts.basic_figure('plots/melki_rates.pdf',
with contexts.subplot(figure, (1, 1, 1), #title='A',
x_label=r'$\rho_d$',
# y_label=r'Non-Boundary Pi Dissociation Rate, $r_d$ [$s^{-1}$]',
y_label=r'$r_d$ [$s^{-1}$]',
logscale_x=True, logscale_y=True) as axes:
contexts.plot(axes, 'plot', cooperativities, rates, 'k.')
# contexts.plot(axes, 'errorbar', cooperativities, rates,
# errors, fmt='k.')
cooperativities = numpy.array(cooperativities)
contexts.plot(axes, 'plot',
cooperativities, 1.0 / (HALFTIME * numpy.sqrt(cooperativities)),
'r-', linewidth=0.5)
axes_2 = axes.twinx()
axes_2.set_yscale('log')
axes_2.set_ylabel(r'$R_d$ [$s^{-1}$]',
size=settings.LABEL_FONT_SIZE)
for label in axes_2.get_yticklabels():
label.set_size(settings.TICK_FONT_SIZE)
axes.axvline(RHO_CRIT, 0, 1, linestyle='--', color='g', linewidth=0.5,
dashes=(3, 3))
axes.set_yticks([1.0e-9, 1.0e-8, 1.0e-7, 1.0e-6, 1.0e-5, 1.0e-4, 1.0e-3])
axes.minorticks_off()
contexts.plot(axes_2, 'plot', cooperativities,
cooperativities * rates, 'k.', markerfacecolor='None')
axes_2.axhline(v_rate_pack[0], 0, 1, linestyle=':', color='b')
axes_2.set_ylim([1.0e-4, 100])
axes_2.set_yticks([1.0e-3, 1.0e-2, 1.0e-1, 1, 1.0e1])
axes_2.minorticks_off()
axes.set_xlim([0.1, 10**11])
axes.set_xticks([1, 100, 10000, 1000000,
100000000, 10000000000])
inset_error_plot(figure)
def melki_rate_plot(figure):
# results = data.load_data('results/melki_rates.dat')
results = data.load_data("results/melki_cooperative_fit.dat")
v_results = data.load_data("results/melki_vectorial_fit.dat")
# results = data.load_data('results/melki_rate_sensitivities.dat')
# cooperativities, rates, statistical_errors, halftimes, hte = results
# cooperativities, rates, errors = results[:3]
cooperativities, rates = results[:2]
v_rate_pack = zip(*v_results)[0][:3]
# with contexts.basic_figure('plots/melki_rates.pdf',
with contexts.subplot(
figure,
(1, 1, 1), # title='A',
x_label=r"$\rho_d$",
# y_label=r'Non-Boundary Pi Dissociation Rate, $r_d$ [$s^{-1}$]',
y_label=r"$r_d$ [$s^{-1}$]",
logscale_x=True,
logscale_y=True,
) as axes:
contexts.plot(axes, "plot", cooperativities, rates, "k.")
# contexts.plot(axes, 'errorbar', cooperativities, rates,
# errors, fmt='k.')
cooperativities = numpy.array(cooperativities)
contexts.plot(
axes, "plot", cooperativities, 1.0 / (HALFTIME * numpy.sqrt(cooperativities)), "r-", linewidth=0.5
)
axes_2 = axes.twinx()
axes_2.set_yscale("log")
axes_2.set_ylabel(r"$R_d$ [$s^{-1}$]", size=settings.LABEL_FONT_SIZE)
for label in axes_2.get_yticklabels():
label.set_size(settings.TICK_FONT_SIZE)
axes.axvline(RHO_CRIT, 0, 1, linestyle="--", color="g", linewidth=0.5, dashes=(3, 3))
axes.set_yticks([1.0e-9, 1.0e-8, 1.0e-7, 1.0e-6, 1.0e-5, 1.0e-4, 1.0e-3])
axes.minorticks_off()
contexts.plot(axes_2, "plot", cooperativities, cooperativities * rates, "k.", markerfacecolor="None")
axes_2.axhline(v_rate_pack[0], 0, 1, linestyle=":", color="b")
axes_2.set_ylim([1.0e-4, 100])
axes_2.set_yticks([1.0e-3, 1.0e-2, 1.0e-1, 1, 1.0e1])
axes_2.minorticks_off()
axes.set_xlim([0.1, 10 ** 11])
axes.set_xticks([1, 100, 10000, 1000000, 100000000, 10000000000])
inset_error_plot(figure)
def random_vectorial_timecourse(figure):
random_results = data.load_data('results/depoly_timecourse_random.dat')
vectorial_results = data.load_data(
'results/depoly_timecourse_vectorial.dat')
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
rtimes, rvalues = numpy.array(random_results[0]), random_results[1:]
vtimes, vvalues = numpy.array(vectorial_results[0]), vectorial_results[1:]
etimes, evals = numpy.array(expt[0]), expt[1]
rtimes -= 300
vtimes -= 300
# with contexts.basic_figure('plots/depoly_rv_timecourses.pdf',
with contexts.subplot(
figure,
(2, 2, 1),
title='A',
# x_label='Time [s]',
y_label=r'Filament Length [$\mu$m]') as axes:
for y in rvalues:
y = numpy.array(y)
y *= 0.0027
contexts.plot(
axes,
'plot',
rtimes,
y,
'-',
color='#FF8080',
# color='#FFA0A0',
linewidth=THIN_LINE)
for y in vvalues:
y = numpy.array(y)
y *= 0.0027
contexts.plot(
axes,
'plot',
vtimes,
y,
'-',
color='#8080FF',
# color='#A0A0FF',
linewidth=THIN_LINE)
contexts.plot(axes, 'plot', etimes, evals, 'k', linewidth=0.7)
axes.set_ylim(0, 15)
axes.set_xlim(0, 1000)
def non_be_quality(figure):
results = data.load_data('results/melki_cooperative_fit.dat')
(cooperativities, rates, min_rates, max_rates, rate_pe,
chi2, min_chi2, max_chi2, chi2_pe) = results
nb_results = data.load_data('results/melki_non_be_coop_fit.dat')
(nbc, nbr, nbminr, nbmaxr, nbrpe, nbchi2, nbminc2,
nbmaxc2, nbcpe) = nb_results
nop_results = data.load_data('results/melki_no_pointed_coop_fit.dat')
(nbc, nopr, junk, junk, jukn,
nopc2, nopminc2, nopmaxc2, junk) = nop_results
v_results = data.load_data('results/melki_vectorial_fit.dat')
(v_rate, junk, junk, junk,
v_chi2, v_min_chi2, v_max_chi2, junk) = zip(*v_results)[0]
v_nb_results = data.load_data('results/melki_non_be_vec_fit.dat')
(v_nbr, v_nbminr, v_nbmaxr, v_nbrpe, v_nbchi2, v_nbminc2,
v_nbmaxc2, v_nbcpe) = zip(*v_nb_results)[0]
with contexts.subplot(figure, (2, 1, 2), title='B',
x_label=r'Pi Dissociation Cooperativity, $\rho_d$',
y_label=r'$\chi^2$ Comparison To Data',
logscale_x=True) as axes:
axes.fill_between([0.1, 1.0e12],
[v_min_chi2, v_min_chi2],
v_max_chi2, color='#AAAAAA')
axes.fill_between([0.1, 1.0e12],
[v_nbminc2, v_nbminc2],
v_nbmaxc2, color='#CCCCFF')
axes.axhline(v_chi2, 0, 1, linestyle=':', color='k')
axes.axhline(v_nbchi2, 0, 1, linestyle=':', color='b')
axes.axvline(RHO_CRIT, 0, 1, linestyle='--', color='g', linewidth=0.5)
contexts.plot(axes, 'errorbar', cooperativities,
chi2, [c - m for c, m in zip(chi2, min_chi2)],
fmt='k.')
contexts.plot(axes, 'errorbar', cooperativities,
nbchi2, [c - m for c, m in zip(nbchi2, nbminc2)],
fmt='b.')
contexts.plot(axes, 'errorbar', cooperativities,
nopc2, [c - m for c, m in zip(nopc2, nopminc2)],
fmt='g.')
axes.set_xlim([0.1, 10**12])
axes.set_ylim([0, 14])
axes.set_xticks([1, 100, 10000, 1000000,
100000000, 10000000000])
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 cooperative_timecourse(figure):
filament_results = data.load_data(
'results/depoly_timecourse_rho_200000.dat')
mean_results = data.load_data('results/depoly_mean_tc.dat')
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
ftimes, fvalues = numpy.array(filament_results[0]), filament_results[1:]
mtimes, mvalues = (numpy.array(mean_results[0]),
0.0027 * numpy.array(mean_results[1]))
etimes, evals = numpy.array(expt[0]), expt[1]
ftimes -= 300
mtimes -= 300
# with contexts.basic_figure('plots/depoly_coop_timecourses.pdf',
with contexts.subplot(figure, (2, 2, 4),
title='D',
x_label='Time [s]'
# y_label=r'Filament Length [$\mu$m]'
) as axes:
for y in fvalues:
y = numpy.array(y)
y *= 0.0027
contexts.plot(
axes,
'plot',
ftimes,
y,
'-',
# color='#FF8080',
# color='#80FF80',
color='#8080FF',
linewidth=THIN_LINE)
contexts.plot(axes, 'plot', etimes, evals, 'k-', linewidth=0.7)
contexts.plot(axes, 'plot', mtimes, mvalues, 'r-', linewidth=0.7)
axes.set_ylim(0, 15)
axes.set_xlim(0, 1000)
def random_vectorial_timecourse(figure):
random_results = data.load_data('results/depoly_timecourse_random.dat')
vectorial_results = data.load_data('results/depoly_timecourse_vectorial.dat')
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
rtimes, rvalues = numpy.array(random_results[0]), random_results[1:]
vtimes, vvalues = numpy.array(vectorial_results[0]), vectorial_results[1:]
etimes, evals = numpy.array(expt[0]), expt[1]
rtimes -= 300
vtimes -= 300
# with contexts.basic_figure('plots/depoly_rv_timecourses.pdf',
with contexts.subplot(figure, (2, 2, 1), title='A',
# x_label='Time [s]',
y_label=r'Filament Length [$\mu$m]') as axes:
for y in rvalues:
y = numpy.array(y)
y *= 0.0027
contexts.plot(axes, 'plot', rtimes, y, '-',
color='#FF8080',
# color='#FFA0A0',
linewidth=THIN_LINE)
for y in vvalues:
y = numpy.array(y)
y *= 0.0027
contexts.plot(axes, 'plot', vtimes, y, '-',
color='#8080FF',
# color='#A0A0FF',
linewidth=THIN_LINE)
contexts.plot(axes, 'plot', etimes, evals, 'k', linewidth=0.7)
axes.set_ylim(0, 15)
axes.set_xlim(0, 1000)
def cooperative_timecourse(figure):
filament_results = data.load_data(
'results/depoly_timecourse_rho_200000.dat')
mean_results = data.load_data('results/depoly_mean_tc.dat')
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
ftimes, fvalues = numpy.array(filament_results[0]), filament_results[1:]
mtimes, mvalues = (numpy.array(mean_results[0]),
0.0027 * numpy.array(mean_results[1]))
etimes, evals = numpy.array(expt[0]), expt[1]
ftimes -= 300
mtimes -= 300
# with contexts.basic_figure('plots/depoly_coop_timecourses.pdf',
with contexts.subplot(figure, (2, 2, 4), title='D',
x_label='Time [s]'
# y_label=r'Filament Length [$\mu$m]'
) as axes:
for y in fvalues:
y = numpy.array(y)
y *= 0.0027
contexts.plot(axes, 'plot', ftimes, y, '-',
# color='#FF8080',
# color='#80FF80',
color='#8080FF',
linewidth=THIN_LINE)
contexts.plot(axes, 'plot', etimes, evals, 'k-', linewidth=0.7)
contexts.plot(axes, 'plot', mtimes, mvalues, 'r-', linewidth=0.7)
axes.set_ylim(0, 15)
axes.set_xlim(0, 1000)
def non_be_rates(figure):
results = data.load_data('results/melki_cooperative_fit.dat')
nb_results = data.load_data('results/melki_non_be_coop_fit.dat')
nop_results = data.load_data('results/melki_no_pointed_coop_fit.dat')
cooperativities, rates, min_rates, max_rates = results[:4]
nbc, nbr, nb_minr, nb_maxr = nb_results[:4]
nopc, nopr, nopminr, nopmaxr = nop_results[:4]
theoretical_rates = 1.0 / (HALFTIME * numpy.sqrt(cooperativities))
errors = numpy.array(numpy.array(rates) -
numpy.array(min_rates)) / theoretical_rates
rates = numpy.array(rates) / theoretical_rates
nberrors = numpy.array(numpy.array(nbr) -
numpy.array(nb_minr)) / theoretical_rates
nbrates = numpy.array(nbr) / theoretical_rates
noperrors = numpy.array(numpy.array(nopr) -
numpy.array(nopminr)) / theoretical_rates
noprates = numpy.array(nopr) / theoretical_rates
with contexts.subplot(
figure,
(2, 1, 1),
title='A',
# x_label=r'Pi Dissociation Cooperativity, $\rho_d$',
y_label=r'$r_d$ [Simulation / Theory]',
logscale_x=True,
) as axes:
axes.axvline(RHO_CRIT, 0, 1, linestyle='--', color='g', linewidth=0.5)
# With enhanced BE dissociation rate
contexts.plot(axes,
'errorbar',
cooperativities,
rates,
errors,
fmt='k.')
# Without enhanced BE dissociation rate
contexts.plot(axes,
'errorbar',
cooperativities,
nbrates,
nberrors,
fmt='b.')
# No pointed end kinetics
contexts.plot(axes,
'errorbar',
cooperativities,
noprates,
noperrors,
fmt='g.')
contexts.plot(axes,
'plot',
cooperativities, [1 for c in cooperativities],
'r-',
linewidth=0.5)
axes.set_xlim([0.1, 10**12])
axes.set_xticks([1, 100, 10000, 1000000, 100000000, 10000000000])
axes.set_ylim([0, 1.2])
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')
