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 melki_sample_timecourses(figure):
data_f_times, data_f = data.load_data("experimental_data/melki_fievez_carlier_1996/factin_concentration.dat")
data_p_times, data_p = data.load_data("experimental_data/melki_fievez_carlier_1996/phosphate_concentration.dat")
data_p = numpy.array(data_p)
sim_f = data.load_data("results/melki_timecourses_f.dat")
sim_p = data.load_data("results/melki_timecourses_p.dat")
sf_times, sf_vals = sim_f[0], sim_f[1:]
sp_times, sp_vals = sim_p[0], sim_p[1:]
colors = ["b", "g", "r", "orange"]
# labels = [r'$\rho_d\to\,\infty$', r'$\rho_d =\,1$', r'$\rho_d =\,10^4$', r'$\rho_d =\,10^8$']
with contexts.subplot(
figure, (1, 1, 1), x_label=r"Time [s]", y_label=r"Concentrations [$\mu$M]" # title='A',
) as axes:
axes.fill_between(data_p_times, data_p * (1 - MELKI_ERROR), data_p * (1 + MELKI_ERROR), color="#CCCCCC")
axes.plot(data_f_times, data_f, "k--")
axes.plot(data_p_times, data_p, "k-", label="Data")
for f, p, c in zip(sf_vals, sp_vals, colors):
axes.plot(sf_times, f, color=c, linestyle="--")
axes.plot(sp_times, p, color=c, linestyle="-")
axes.set_ylim([0, 35])
axes.set_xlim([0, 2500])
inset_qof(figure)
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 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 melki_sample_timecourses(figure):
data_f_times, data_f = data.load_data(
'experimental_data/melki_fievez_carlier_1996/factin_concentration.dat')
data_p_times, data_p = data.load_data(
'experimental_data/melki_fievez_carlier_1996/phosphate_concentration.dat')
data_p = numpy.array(data_p)
sim_f = data.load_data('results/melki_timecourses_f.dat')
sim_p = data.load_data('results/melki_timecourses_p.dat')
sf_times, sf_vals = sim_f[0], sim_f[1:]
sp_times, sp_vals = sim_p[0], sim_p[1:]
colors = ['b', 'g', 'r', 'orange']
# labels = [r'$\rho_d\to\,\infty$', r'$\rho_d =\,1$', r'$\rho_d =\,10^4$', r'$\rho_d =\,10^8$']
with contexts.subplot(figure, (1, 1, 1), #title='A',
x_label=r'Time [s]',
y_label=r'Concentrations [$\mu$M]') as axes:
axes.fill_between(data_p_times,
data_p * (1 - MELKI_ERROR), data_p * (1 + MELKI_ERROR),
color='#CCCCCC')
axes.plot(data_f_times, data_f, 'k--')
axes.plot(data_p_times, data_p, 'k-', label='Data')
for f, p, c in zip(sf_vals, sp_vals, colors):
axes.plot(sf_times, f, color=c, linestyle='--')
axes.plot(sp_times, p, color=c, linestyle='-')
axes.set_ylim([0, 35])
axes.set_xlim([0, 2500])
inset_qof(figure)
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 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 vec_tcs(figure):
data_f_times, data_f = data.load_data(
'experimental_data/melki_fievez_carlier_1996/factin_concentration.dat')
data_p_times, data_p = data.load_data(
'experimental_data/melki_fievez_carlier_1996/phosphate_concentration.dat')
data_p = numpy.array(data_p)
sim_p = data.load_data('results/melki_timecourses_p.dat')
sp_times, sp_vals = sim_p[0], sim_p[1]
nbesim_p = data.load_data('results/melki_nonbe_tc_p.dat')
colors = ['b', 'g', 'r', 'orange']
labels = ['Vectorial', 'Random', r'$\rho_d = 10^4$', r'$\rho_d = 10^8$']
with contexts.subplot(figure, (1, 1, 1),
x_label=r'Time [s]',
y_label=r'Concentrations [$\mu$M]') as axes:
axes.fill_between(data_p_times,
data_p * (1 - MELKI_ERROR), data_p * (1 + MELKI_ERROR),
color='#CCCCCC')
axes.plot(data_f_times, data_f, 'k--')
axes.plot(data_p_times, data_p, 'k-', label='Data')
# BE included
axes.plot(sp_times, sp_vals, color='b', linestyle='-', label='BE')
# Non-BE
axes.plot(nbesim_p[0], nbesim_p[1], color='r', linestyle='-', label='Non-BE')
axes.set_ylim([0, 35])
axes.set_xlim([0, 2500])
axes.legend(loc=4)
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 stacked_timecourses(figure):
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
with contexts.subplot(
figure, (1, 1, 1)
# x_label=r'Time [s]',
# y_label=r'Filament Length [$\mu$M]'
) as axes:
plot_rv_traces(axes, expt)
plot_be_traces(axes, expt)
plot_coop_traces(axes, expt)
plot_fast_traces(axes, expt)
axes.set_xlim([0, 1100])
axes.set_ylim([0, 30])
axes.set_xticks([])
# axes.set_xticks([0, 200, 400, 600, 800, 1000])
axes.set_yticks([])
axes.set_frame_on(False)
add_scale_bar(
axes,
(200, 2),
# scale_position=(0.875, 0.675))
scale_position=(0.05, 0.45))
inset_qof(figure)
def normal_timecourses(figure):
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
with contexts.subplot(figure, (2, 2, 1),
title='A',
x_label=r'Time [s]',
y_label=r'Filament Length [$\mu$M]') as axes:
# Random filaments
plot_traces(axes,
'results/depoly_tc_random.dat',
color='#80CC80',
thickness=THIN_LINE)
# Vectorial filaments
plot_traces(
axes,
'results/depoly_tc_vectorial.dat',
color='#BBBB80',
# color='#8080FF',
thickness=THIN_LINE)
# Vec non be
# plot_traces(top_left_axes,
# 'results/depoly_nonbe_tc.dat',
# color='#80FF80', thickness=THIN_LINE)
# Data on top
axes.plot(expt[0], expt[1], 'k.', markersize=1, clip_on=False)
def normal_timecourses(figure):
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
with contexts.subplot(figure, (2, 2, 1), title='A',
x_label=r'Time [s]',
y_label=r'Filament Length [$\mu$M]') as axes:
# Random filaments
plot_traces(axes,
'results/depoly_tc_random.dat',
color='#80CC80',
thickness=THIN_LINE)
# Vectorial filaments
plot_traces(axes,
'results/depoly_tc_vectorial.dat',
color='#BBBB80',
# color='#8080FF',
thickness=THIN_LINE)
# Vec non be
# plot_traces(top_left_axes,
# 'results/depoly_nonbe_tc.dat',
# color='#80FF80', thickness=THIN_LINE)
# Data on top
axes.plot(expt[0], expt[1], 'k.',
markersize=1, clip_on=False)
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 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 coop_timecourses(figure):
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
with contexts.subplot(
figure,
(1, 2, 2), #title='E',
x_label=r'Time [s]') as axes:
axes.plot(expt[0], expt[1], 'k.', markersize=1, clip_on=False)
def coop_timecourses(figure):
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
with contexts.subplot(figure, (1, 2, 2), #title='E',
x_label=r'Time [s]'
) as axes:
axes.plot(expt[0], expt[1], 'k.',
markersize=1, clip_on=False)
def linear_lagtime_plot(
figure,
coop_linestyles=[
'k--', # Random
'r--', # XXX Consider making green
'b--',
'g--'
],
cooperative_filename='results/fnc_cooperative_lagtimes.dat',
vectorial_filename='results/fnc_vectorial_lagtimes.dat',
data_filename='experimental_data/carlier_1986/lagtimes.dat'):
d_fncs, d_lagtimes = data.load_data(data_filename)
d_norm_lagtimes = d_lagtimes[0] / numpy.array(d_lagtimes)
coop_data = data.load_data(cooperative_filename)
coop_fncs, coop_lagtimes = coop_data[0], coop_data[1:]
coop_fncs = numpy.array(coop_fncs) * 1000
v_fncs, v_lagtimes = data.load_data(vectorial_filename)
v_fncs = numpy.array(v_fncs) * 1000
# index 0 is at fnc = 1.1 nM
v_lagtimes = v_lagtimes[0] / numpy.array(v_lagtimes)
th_fncs = numpy.linspace(1, 20, 100)
v_theory = th_fncs / 1.1
r_theory = numpy.ones(len(th_fncs))
with contexts.subplot(
figure,
(1, 1, 1), #title='A',
x_label=r'$n$ [nM]',
y_label=r'$t_{lag}^{-1}$ [AU]') as axes:
# Cooperative simulations
for lagtimes, linestyle in zip(coop_lagtimes, coop_linestyles):
# index 0 is at fnc = 1.1 nM
y = lagtimes[0] / numpy.array(lagtimes)
axes.plot(coop_fncs, y, linestyle, dashes=(3, 3.5))
# Vectorial simulations
axes.plot(v_fncs, v_lagtimes, 'k--', dashes=(3, 3.5))
# Vectorial theory
axes.plot(th_fncs, v_theory, 'k-', linewidth=0.5)
# Random theory
axes.plot(th_fncs, r_theory, 'k-', linewidth=0.5)
# Carlier 1986 data
axes.plot(d_fncs, d_norm_lagtimes, 'ko')
axes.set_xlim([0, 22])
x_ticks = [1.1, 5, 10, 15, 20]
axes.set_xticks(x_ticks)
axes.set_xticklabels(map(str, x_ticks))
inset_qof_plot(figure)
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 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 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 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 timecourse_plot(figure, xmax=3600, ymax=35):
timecourses = data.load_data('results/pi_saturation_timecourse.dat')
with contexts.subplot(figure, (2, 2, 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-.')
# adp-pi
contexts.plot(axes, 'plot', timecourses[0], timecourses[3], 'g-')
# pi
contexts.plot(axes, 'plot', timecourses[0], timecourses[5], 'r-')
axes.set_xlim(0, xmax)
axes.set_ylim(0, ymax)
# Lines to highlight halftime
axes.axhline(15,
0,
float(TIMECOURSE_HALFTIME) / xmax,
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),
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,
'[ADP-Pi-actin]',
horizontalalignment='right',
verticalalignment='bottom',
size=settings.SMALL_FONT_SIZE)
def linear_lagtime_plot(figure,
coop_linestyles=['k--', # Random
'r--', # XXX Consider making green
'b--',
'g--'
],
cooperative_filename='results/fnc_cooperative_lagtimes.dat',
vectorial_filename='results/fnc_vectorial_lagtimes.dat',
data_filename='experimental_data/carlier_1986/lagtimes.dat'):
d_fncs, d_lagtimes = data.load_data(data_filename)
d_norm_lagtimes = d_lagtimes[0] / numpy.array(d_lagtimes)
coop_data = data.load_data(cooperative_filename)
coop_fncs, coop_lagtimes = coop_data[0], coop_data[1:]
coop_fncs = numpy.array(coop_fncs) * 1000
v_fncs, v_lagtimes = data.load_data(vectorial_filename)
v_fncs = numpy.array(v_fncs) * 1000
# index 0 is at fnc = 1.1 nM
v_lagtimes = v_lagtimes[0] / numpy.array(v_lagtimes)
th_fncs = numpy.linspace(1, 20, 100)
v_theory = th_fncs / 1.1
r_theory = numpy.ones(len(th_fncs))
with contexts.subplot(figure, (1, 1, 1), #title='A',
x_label=r'$n$ [nM]',
y_label=r'$t_{lag}^{-1}$ [AU]') as axes:
# Cooperative simulations
for lagtimes, linestyle in zip(coop_lagtimes, coop_linestyles):
# index 0 is at fnc = 1.1 nM
y = lagtimes[0] / numpy.array(lagtimes)
axes.plot(coop_fncs, y, linestyle, dashes=(3,3.5))
# Vectorial simulations
axes.plot(v_fncs, v_lagtimes, 'k--', dashes=(3,3.5))
# Vectorial theory
axes.plot(th_fncs, v_theory, 'k-', linewidth=0.5)
# Random theory
axes.plot(th_fncs, r_theory, 'k-', linewidth=0.5)
# Carlier 1986 data
axes.plot(d_fncs, d_norm_lagtimes, 'ko')
axes.set_xlim([0, 22])
x_ticks = [1.1, 5, 10, 15, 20]
axes.set_xticks(x_ticks)
axes.set_xticklabels(map(str, x_ticks))
inset_qof_plot(figure)
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 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 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 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 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 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 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 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 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 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 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 vec_tcs(figure):
data_f_times, data_f = data.load_data(
'experimental_data/melki_fievez_carlier_1996/factin_concentration.dat')
data_p_times, data_p = data.load_data(
'experimental_data/melki_fievez_carlier_1996/phosphate_concentration.dat'
)
data_p = numpy.array(data_p)
sim_p = data.load_data('results/melki_timecourses_p.dat')
sp_times, sp_vals = sim_p[0], sim_p[1]
nbesim_p = data.load_data('results/melki_nonbe_tc_p.dat')
colors = ['b', 'g', 'r', 'orange']
labels = ['Vectorial', 'Random', r'$\rho_d = 10^4$', r'$\rho_d = 10^8$']
with contexts.subplot(figure, (1, 1, 1),
x_label=r'Time [s]',
y_label=r'Concentrations [$\mu$M]') as axes:
axes.fill_between(data_p_times,
data_p * (1 - MELKI_ERROR),
data_p * (1 + MELKI_ERROR),
color='#CCCCCC')
axes.plot(data_f_times, data_f, 'k--')
axes.plot(data_p_times, data_p, 'k-', label='Data')
# BE included
axes.plot(sp_times, sp_vals, color='b', linestyle='-', label='BE')
# Non-BE
axes.plot(nbesim_p[0],
nbesim_p[1],
color='r',
linestyle='-',
label='Non-BE')
axes.set_ylim([0, 35])
axes.set_xlim([0, 2500])
axes.legend(loc=4)
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 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 qof_plot(figure):
coop_qof = data.load_data('results/fnc_cooperative_qof.dat')
vec_qof = data.load_data('results/fnc_vectorial_qof.dat')
coops, qof, min_qof, max_qof, pct_err = coop_qof
err = [m - q for m, q in zip(max_qof, qof)]
with contexts.subplot(figure, (2, 1, 2),
title='B',
x_label=r'$\rho_d$',
y_label=r'Lag-time Quality of Fit, $\chi^2$',
logscale_x=True,
logscale_y=False) as axes:
axes.fill_between([0.1, 1.0e12], [vec_qof[1][0], vec_qof[1][0]],
vec_qof[2][0],
color='#CCCCFF')
axes.axhline(0, 0, 1, color='k', linestyle='-', linewidth=0.5)
axes.axhline(vec_qof[0], 0, 1, color='b', linestyle=':')
axes.axvline(RHO_CRIT, 0, 1, color='g', linestyle='--', linewidth=0.5)
axes.errorbar(coops, qof, err, fmt='k.')
axes.set_ylim([-5, None])
axes.set_xlim([0.1, 10**12])
axes.set_xticks([1, 100, 10000, 1000000, 100000000, 10000000000])
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 coop_qof(figure):
coop_data = data.load_data('results/depoly_cooperative_qof.dat')
vec_data = data.load_data('results/depoly_vectorial_qof.dat')
rhos, chis, minchis, maxchis, pct_chis = coop_data
errs = numpy.array(chis) - numpy.array(minchis)
vchi, vminchi, vmaxchi, vpct_chi = zip(*vec_data)[0]
with contexts.subplot(figure, (2, 2, 3), title='E',
x_label=r'$\rho_d$',
y_label=r'Quality of Fit, $\Delta^2$',
logscale_x=True) as axes:
axes.fill_between([0.1, 1.0e12], [vminchi, vminchi],
vmaxchi, color='#CCCCFF')
axes.axhline(vchi, 0, 1, color='b', linestyle=':')
axes.plot(rhos, chis, 'k.')
axes.errorbar(rhos, chis, errs, fmt='k.')
axes.set_ylim([0, 8])
axes.set_xlim([0.1, 10**12])
axes.set_xticks([1, 100, 10000, 1000000,
100000000, 10000000000])
def stacked_timecourses(figure):
expt = data.load_data(
'experimental_data/jegou_2011/sample_filament_timecourse.dat')
with contexts.subplot(figure, (1, 1, 1)
# x_label=r'Time [s]',
# y_label=r'Filament Length [$\mu$M]'
) as axes:
plot_rv_traces(axes, expt)
plot_be_traces(axes, expt)
plot_coop_traces(axes, expt)
plot_fast_traces(axes, expt)
axes.set_xlim([0, 1100])
axes.set_ylim([0, 30])
axes.set_xticks([])
# axes.set_xticks([0, 200, 400, 600, 800, 1000])
axes.set_yticks([])
axes.set_frame_on(False)
add_scale_bar(axes, (200, 2),
# scale_position=(0.875, 0.675))
scale_position=(0.05, 0.45))
inset_qof(figure)
def qof_plot(figure):
coop_qof = data.load_data('results/fnc_cooperative_qof.dat')
vec_qof = data.load_data('results/fnc_vectorial_qof.dat')
coops, qof, min_qof, max_qof, pct_err = coop_qof
err = [m - q for m, q in zip(max_qof, qof)]
with contexts.subplot(figure, (2, 1, 2), title='B',
x_label=r'$\rho_d$',
y_label=r'Lag-time Quality of Fit, $\chi^2$',
logscale_x=True, logscale_y=False) as axes:
axes.fill_between([0.1, 1.0e12],
[vec_qof[1][0], vec_qof[1][0]],
vec_qof[2][0],
color='#CCCCFF')
axes.axhline(0, 0, 1, color='k', linestyle='-', linewidth=0.5)
axes.axhline(vec_qof[0], 0, 1, color='b', linestyle=':')
axes.axvline(RHO_CRIT, 0, 1, color='g', linestyle='--', linewidth=0.5)
axes.errorbar(coops, qof, err, fmt='k.')
axes.set_ylim([-5, None])
axes.set_xlim([0.1, 10**12])
axes.set_xticks([1, 100, 10000, 1000000,
100000000, 10000000000])
def coop_qof(figure):
coop_data = data.load_data('results/depoly_cooperative_qof.dat')
vec_data = data.load_data('results/depoly_vectorial_qof.dat')
rhos, chis, minchis, maxchis, pct_chis = coop_data
errs = numpy.array(chis) - numpy.array(minchis)
vchi, vminchi, vmaxchi, vpct_chi = zip(*vec_data)[0]
with contexts.subplot(figure, (2, 2, 3),
title='E',
x_label=r'$\rho_d$',
y_label=r'Quality of Fit, $\Delta^2$',
logscale_x=True) as axes:
axes.fill_between([0.1, 1.0e12], [vminchi, vminchi],
vmaxchi,
color='#CCCCFF')
axes.axhline(vchi, 0, 1, color='b', linestyle=':')
axes.plot(rhos, chis, 'k.')
axes.errorbar(rhos, chis, errs, fmt='k.')
axes.set_ylim([0, 8])
axes.set_xlim([0.1, 10**12])
axes.set_xticks([1, 100, 10000, 1000000, 100000000, 10000000000])
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])