def plot_ss(this, save=False, filename=None):
"""
Plot the nonequilibrium steady-state distribution associated
with a Simulation object.
By default, this will plot the eigenvector-derived steady-state distribution.
"""
fig = plt.figure(figsize=(6 * 1.2, 6))
gs = GridSpec(1, 1, wspace=0.2, hspace=0.5)
ax1 = plt.subplot(gs[0, 0])
ax1.plot(range(this.bins), this.ss[0:this.bins], c=this.unbound_clr)
ax1.plot(range(this.bins),
this.ss[this.bins:2 * this.bins],
c=this.bound_clr,
ls='--')
ax1.set_xticks(
[0, this.bins / 4, this.bins / 2, 3 * this.bins / 4, this.bins])
ax1.set_xticklabels([
r'$-\pi$', r'$-\frac{1}{2}\pi{}$', r'$0$', r'$\frac{1}{2}\pi$',
r'$\pi$'
])
ax1.set_xlabel(r'$\theta$ (rad)')
ax1.set_ylabel(r'$p$ (probability)')
paper_plot(fig, scientific=False)
if save:
plt.savefig(filename + '.png', dpi=300, bbox_inches='tight')
def plot_histograms(df, concentration, color):
"""
Plot histogram of the directional flux values for a given concentration.
"""
tmp = return_concentration_slice(df, concentration)
flux = tmp['Directional flux'].abs().values
hist, bin_edges = np.histogram(flux, bins=20)
mids = bin_edges[1:] - np.diff(bin_edges) / 2
print('Edge, Edge, Count')
for i in range(len(hist)):
print(bin_edges[i], bin_edges[i + 1], hist[i])
fig = plt.figure(figsize=(6 * 1.2, 6))
gs = GridSpec(1, 1, wspace=0.2, hspace=0.5)
ax = plt.subplot(gs[0, 0])
rects = ax.bar(mids, hist, width=10, color=color)
def autolabel(rects):
for rect in rects:
height = rect.get_height()
i = rects.index(rect)
plt.text(rect.get_x() + rect.get_width() / 2.,
1.05 * height,
'{}'.format(int(height)),
ha='center',
va='bottom')
autolabel(rects)
ax.set_ylabel('Count')
ax.set_xlabel('Directional flux (cycle s$^{{-1}}$)')
paper_plot(fig)
def plot_load_over_threshold(concentrations,
number_above_thresholds,
colors,
names,
annotation=None,
annotation_x=None,
annotation_y=None,
xmin=10**-6,
xmax=10**-2,
ymin=0,
ymax=140):
"""
Plot the number of angles with stall force (i.e., load) over a certain threshold.
"""
fig = plt.figure(figsize=(6 * 1.2, 6))
gs = GridSpec(1, 1, wspace=0.2, hspace=0.5)
ax = plt.subplot(gs[0, 0])
for system, color, name in zip(number_above_thresholds, colors, names):
ax.step(concentrations, system, ls='-', c=color, label=name)
if annotation:
ax.annotate(annotation,
xy=(0.5, 0.5),
xytext=(annotation_x, annotation_y),
xycoords='figure fraction',
fontsize=20)
ax.legend(loc='upper left', frameon=True, framealpha=1.0, edgecolor='k')
ax.set_xlabel('Substrate concentration (M)')
ax.set_ylabel('Number over threshold')
ax.set_xscale('log')
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
paper_plot(fig)
def plot_input(this, save=False, filename=None, label=False, title=None):
"""
Plot the apo and bound histograms as a function of dihedral angle.
The input histograms are taken to be normalized populations derived from MD simulations.
"""
fig = plt.figure(figsize=(6 * 1.2, 6))
gs = GridSpec(1, 1, wspace=0.2, hspace=0.5)
ax1 = plt.subplot(gs[0, 0])
ax1.plot(range(this.bins),
this.unbound_population,
c=this.unbound_clr,
label='Apo' if label else '')
ax1.plot(range(this.bins),
this.bound_population,
c=this.bound_clr,
ls='--',
label='Bound' if label else '')
ax1.set_xticks(
[0, this.bins / 4, this.bins / 2, 3 * this.bins / 4, this.bins])
ax1.set_xticklabels([
r'$-\pi$', r'$-\frac{1}{2}\pi{}$', r'$0$', r'$\frac{1}{2}\pi$',
r'$\pi$'
])
ax1.set_xlabel(r'$\theta$ (rad)')
ax1.set_ylabel(r'$p$ (input population)')
if label:
ax1.legend(frameon=True, loc=0, framealpha=1.0, edgecolor='k')
if title:
ax1.set_title(title)
paper_plot(fig, scientific=False)
if save:
plt.savefig(filename + '.png', dpi=300, bbox_inches='tight')
def plot_flux_over_threshold(concentrations,
number_above_thresholds,
colors,
names,
threshold_labels=None,
xmin=10**-6,
xmax=10**-2,
ymin=0,
ymax=140):
"""
Plot the number of angles with probability flux over a certain threshold.
"""
fig = plt.figure(figsize=(6 * 1.2, 6))
gs = GridSpec(1, 1, wspace=0.2, hspace=0.5)
ax = plt.subplot(gs[0, 0])
linestyles = ['-', '--']
# For simplicity, I think I should enforce paired plotting. That is, to
# plot the number of angles over two thresholds for each system, with the
# line styles given above. We should be able to handle an arbitrary number
# of pairs.
pairs = [
number_above_thresholds[x:x + 2]
for x in range(0, len(number_above_thresholds), 2)
]
for system, color, name in zip(pairs, colors, names):
ax.plot(concentrations, system[0], ls='-', c=color, label=name)
ax.plot(concentrations, system[1], ls='--', c=color)
handles, labels = ax.get_legend_handles_labels()
display = (0, 1, 2)
artists = []
if threshold_labels:
for threshold_label, style in zip(threshold_labels, linestyles):
artists.append(
plt.Line2D((0, 1), (0, 0), color='k', linestyle=style))
ax.legend([handle
for i, handle in enumerate(handles) if i in display] +
artists,
[label for i, label in enumerate(labels) if i in display] +
threshold_labels,
loc='upper left',
frameon=True,
framealpha=1.0,
edgecolor='k')
ax.set_xlabel('Substrate concentration (M)')
ax.set_ylabel('Number over threshold')
ax.set_xscale('log')
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
paper_plot(fig)
def plot_energy(this, save=False, filename=None):
"""
Plot the unbound and bound energies (i.e., chemical potentials)
associated with a Simulation object.
"""
fig = plt.figure(figsize=(6 * 1.2, 6))
gs = GridSpec(1, 1, wspace=0.2, hspace=0.5)
ax1 = plt.subplot(gs[0, 0])
ax1.plot(range(this.bins), this.unbound, c=this.unbound_clr)
ax1.plot(range(this.bins), this.bound, c=this.bound_clr, ls='--')
ax1.set_xticks(
[0, this.bins / 4, this.bins / 2, 3 * this.bins / 4, this.bins])
ax1.set_xticklabels([
r'$-\pi$', r'$-\frac{1}{2}\pi{}$', r'$0$', r'$\frac{1}{2}\pi$',
r'$\pi$'
])
ax1.set_xlabel(r'$\theta$ (rad)')
ax1.set_ylabel(r'$\mu$ (kcal mol$^{-1}$)')
paper_plot(fig, scientific=False)
if save:
plt.savefig(filename + '.png', dpi=300, bbox_inches='tight')
def plot_load(this, save=False, filename=None, label=False, title=None):
"""
Plot the unbound and bound energy surfaces with a constant added load.
"""
fig = plt.figure(figsize=(6 * 1.2, 6))
gs = GridSpec(1, 1, wspace=0.2, hspace=0.5)
ax1 = plt.subplot(gs[0, 0])
ax1.plot(
range(this.bins),
[this.unbound[i] + this.load_function(i) for i in range(this.bins)],
c='k',
ls='--',
lw=2,
label='Apo with load' if label else '')
ax1.plot(range(this.bins), this.unbound, c=this.unbound_clr)
ax1.plot(range(this.bins),
[this.bound[i] + this.load_function(i) for i in range(this.bins)],
c='k',
ls='--',
lw=2,
label='Bound with load' if label else '')
ax1.plot(range(this.bins), this.bound, c=this.bound_clr, ls='--')
ax1.set_xticks(
[0, this.bins / 4, this.bins / 2, 3 * this.bins / 4, this.bins])
ax1.set_xticklabels([
r'$-\pi$', r'$-\frac{1}{2}\pi{}$', r'$0$', r'$\frac{1}{2}\pi$',
r'$\pi$'
])
ax1.set_xlabel(r'$\theta$ (rad)')
ax1.set_ylabel(r'Free energy (kcal mol$^{-1}$)')
if label:
ax1.legend(frameon=True, loc=0, framealpha=1.0, edgecolor='k')
if title:
ax1.set_title(title)
paper_plot(fig, scientific=False)
if save:
plt.savefig(filename + '.png', dpi=300, bbox_inches='tight')
def plot_flux(this,
save=False,
filename=None,
label=False,
title=None,
zero_crossing=False):
"""
Plot the intrasurface flux separately and as a sum. The intrasurface flux
is the directional flux. This also prints the simulation parameters.
"""
print_parameter('C', this.C_intersurface, 'second**-1')
print_parameter('D', this.D, 'degrees**2 second**-1')
print_parameter('k_{cat}', this.catalytic_rate, 'second**-1')
print_parameter('[S]', this.cSubstrate, 'M')
print_parameter('dt', this.dt, 'second')
print('-' * 25)
print_parameter('Intrasurface flux', np.mean(this.flux_u + this.flux_b),
'cycle second**-1')
print_parameter('Peak', np.max(np.hstack((this.flux_u, this.flux_b))),
'cycle second**-1')
if this.load:
print('-' * 25)
applied_load = this.load_slope
power = applied_load * np.mean(this.flux_u + this.flux_b)
print_parameter('Applied load', applied_load, 'kcal mol**-1 cycle**-1')
print_parameter('Power generated', power, 'kcal mol**-1 second**-1')
fig = plt.figure(figsize=(6 * 1.2, 6))
gs = GridSpec(1, 1, wspace=0.2, hspace=0.5)
ax1 = plt.subplot(gs[0, 0])
ax1.plot(range(this.bins),
this.flux_u,
c=this.unbound_clr,
label='Apo' if label else '')
ax1.plot(range(this.bins),
this.flux_b,
c=this.bound_clr,
ls='--',
label='Bound' if label else '')
ax1.plot(range(this.bins),
this.flux_u + this.flux_b,
'o',
c='k',
lw=2,
alpha=0.5,
zorder=-1,
label='Net flux')
if zero_crossing:
ax1.axhline(y=0, ls=':', lw=1, c='k')
ax1.set_xticks(
[0, this.bins / 4, this.bins / 2, 3 * this.bins / 4, this.bins])
ax1.set_xticklabels([
r'$-\pi$', r'$-\frac{1}{2}\pi{}$', r'$0$', r'$\frac{1}{2}\pi$',
r'$\pi$'
])
ax1.set_xlabel(r'$\theta$ (rad)')
ax1.set_ylabel('Flux $J$ (cycle s$^{-1}$)')
ax1.legend(frameon=True, loc=0, framealpha=1.0, edgecolor='k')
if title:
ax1.set_title(title)
paper_plot(fig, scientific=False)
if save:
plt.savefig(filename + '.png', dpi=300, bbox_inches='tight')