def _heatmap_alignment(gs, alignment, col_idx):
xlim, ylim = FigureUtil._limits(alignment._all_fecs)
max_x = xlim[1]
bin_step_nm = 1
bin_step_pN = 5
bins_x = np.arange(xlim[0], xlim[1] + bin_step_nm, step=bin_step_nm)
bins_y = np.arange(ylim[0], ylim[1] + bin_step_pN, step=bin_step_pN)
common_kw = dict(separation_max=max_x,
use_colorbar=False,
title="",
bins=(bins_x, bins_y))
ax1 = plt.subplot(gs[0, col_idx])
FEC_Plot.heat_map_fec(alignment.zeroed.fec_list, **common_kw)
FigureUtil._plot_fmt(ax1, xlim, ylim, color=True)
ax2 = plt.subplot(gs[1, col_idx])
FEC_Plot.heat_map_fec(alignment.polished.fec_list, **common_kw)
FigureUtil._plot_fmt(ax2, xlim, ylim, color=True)
title_kw = dict(color='b', y=0.95, loc='left', fontsize=6)
downarrow = "$\Downarrow$"
title_sub = downarrow + " Subtract $X_{\mathbf{PEG3400}}(F)$ + " + \
"$L_{\mathbf{0,C-term}}$"
PlotUtilities.title(title_sub, **title_kw)
PlotUtilities.no_x_label(ax=ax2)
ax3 = plt.subplot(gs[2, col_idx])
FEC_Plot.heat_map_fec(alignment.blacklisted.fec_list, **common_kw)
FigureUtil._plot_fmt(ax3, xlim, ylim, is_bottom=True, color=True)
PlotUtilities.title(downarrow + " Remove poorly-fit FECs", **title_kw)
return [ax1, ax2, ax3]
def _make_algned_plot(alignment, label):
fig = PlotUtilities.figure((3, 4))
gs = gridspec.GridSpec(3, 2)
# make the 'standard' alignment plots
axes = _heatmap_alignment(gs, alignment, 0)
_ensemble_alignment(gs, alignment, 1)
out_name = "./FigureS_Alignment_{:s}.png".format(label)
PlotUtilities.title(label, ax=axes[0])
PlotUtilities.savefig(fig,
out_name,
subplots_adjust=dict(hspace=0.12, wspace=0.02))
def data_plot(fecs, energies, gs1=None, xlim=[None, None]):
n_cols = len(energies)
n_rows = 2
all_ax = []
if gs1 is None:
gs1 = gridspec.GridSpec(n_rows + 1, n_cols)
for i, (data, e) in enumerate(zip(fecs, energies)):
axs_tmp = [plt.subplot(gs1[j, i]) for j in range(n_rows)]
ax1, ax2 = axs_tmp
PlotUtil.plot_landscapes(data, e, ax1=ax1, ax2=ax2)
# every axis after the first gets more of the decoaration chopped...
all_ax.append(axs_tmp)
# XXX should really put this into the meta class...
plt.sca(ax1)
tmp_str = e.file_name
match = re.search(
r"""
([\w+-]+Retinal)/([\d\w]+)/([\d\w]+)/
""", tmp_str, re.IGNORECASE | re.VERBOSE)
assert match is not None
groups = match.groups()
BR, velocity, title = groups
vel_label = velocity.replace("nms", "")
title_label = title.replace("FEC", "")
title = "{:s}, v={:s}\n {:s}".format(BR, vel_label, title_label)
PlotUtilities.title(title, fontsize=5)
for a in axs_tmp:
a.set_xlim(xlim)
fix_axes(all_ax)
xlim = all_ax[0][0].get_xlim()
# just get the IWT
energies_plot = [e._iwt_obj for e in energies]
q_interp, splines = RetinalUtil.interpolating_G0(energies_plot)
# get an average/stdev of energy
ax = plt.subplot(gs1[-1, 0])
mean_energy, std_energy = PlotUtil.plot_mean_landscape(q_interp,
splines,
ax=ax)
max_q_nm = RetinalUtil.q_GF_nm_plot()
q_at_max_energy,_,_ = \
PlotUtil.plot_delta_GF(q_interp,mean_energy,std_energy,
max_q_nm=max_q_nm)
plt.axvspan(q_at_max_energy, max(xlim), color='k', alpha=0.3)
ax.set_xlim(xlim)
def _make_work_plot(fec_list,x_arr,works_kcal,gs,col,color,title):
# get the interpolated work
x_interp, mean_W, std_W = _mean_work(x_arr, works_kcal)
# use Davids function
shift_david_nm_plot = 10
L0_david = 11e-9
max_david = L0_david * 0.95
x_david = np.linspace(0,max_david,num=100)
style_david = dict(color='b',linestyle='--',label="David")
legend_kw = dict(handlelength=1.5,handletextpad=0.3,fontsize=6)
david_F = _f_david(kbT=4.1e-21, L0=L0_david, Lp=0.4e-9, x=x_david)
david_W = _single_work(x=x_david,f=david_F)
x_david_plot = x_david * 1e9 - shift_david_nm_plot
W_david_plot = david_W * kcal_per_mol_per_J()
f_david_plot = david_F * 1e12
is_left = (col == 0)
fmt_kw = dict(is_left=is_left)
label_work = "$W$ (kcal/mol)"
# interpolate each work onto a grid
xlim, ylim, ylim_work = xlim_ylim()
fudge_work = max(std_W)
ax1 = plt.subplot(gs[0,col])
FigureUtil._plot_fec_list(fec_list,xlim,ylim)
plt.plot(x_david_plot,f_david_plot,**style_david)
if is_left:
PlotUtilities.legend(**legend_kw)
FigureUtil._plot_fmt(ax1, xlim, ylim,**fmt_kw)
PlotUtilities.title(title,color=color)
ax2 = plt.subplot(gs[1,col])
for x,w in zip(x_arr,works_kcal):
plt.plot(x * 1e9,w,linewidth=0.75)
FigureUtil._plot_fmt(ax2, xlim, ylim_work,ylabel=label_work,**fmt_kw)
ax3 = plt.subplot(gs[2,col])
_plot_mean_works(x_interp, mean_W, std_W, color, title)
style_lower_david = dict(**style_david)
if (not is_left):
style_lower_david['label'] = None
plt.plot(x_david_plot,W_david_plot,'b--',zorder=5,**style_lower_david)
PlotUtilities.legend(**legend_kw)
FigureUtil._plot_fmt(ax3, xlim, ylim_work,is_bottom=True,
ylabel=label_work,**fmt_kw)
def _plot_comparison(plot_dir, heatmap_jcp, iwt_obj, data_sliced_plot):
fmt = dict(xlim=[-20, 55], ylim=[-20, 150])
_G0_plot(plot_dir, data_sliced_plot, iwt_obj, fmt=fmt)
fig = FigureUtil._fig_single(y=6)
ax1 = plt.subplot(2, 1, 1)
extent = heatmap_jcp._extent_nm_and_pN(offset_x_nm=0)
plt.imshow(heatmap_jcp.heatmap,
origin='lower',
aspect='auto',
extent=extent,
cmap=plt.cm.afmhot)
FigureUtil._plot_fmt(is_bottom=False, ax=ax1, **fmt)
PlotUtilities.title("Top: JCP.\n Bottom: New data, - PEG3400")
ax2 = plt.subplot(2, 1, 2)
FEC_Plot.heat_map_fec(data_sliced_plot,
use_colorbar=False,
num_bins=(150, 75),
separation_max=fmt['xlim'][1])
FigureUtil._plot_fmt(is_bottom=True, ax=ax2, **fmt)
out_name = plot_dir + "FigureSX_jcp_fec_comparison.png"
PlotUtilities.savefig(fig, out_name, tight=True)
def _plot_fmt(ax,
xlim,
ylim,
is_bottom=False,
color=False,
is_left=True,
ylabel="$F$ (pN)",
xlabel="Extension (nm)"):
PlotUtilities.tickAxisFont(ax=ax)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
PlotUtilities.title("", ax=ax)
PlotUtilities.ylabel(ylabel, ax=ax)
PlotUtilities.xlabel(xlabel, ax=ax)
if (not is_bottom):
PlotUtilities.no_x_label(ax=ax)
PlotUtilities.xlabel("", ax=ax)
if (not is_left):
PlotUtilities.no_y_label(ax=ax)
PlotUtilities.ylabel("", ax=ax)
if color:
color_kw = dict(ax=ax, color='w', label_color='k')
PlotUtilities.color_x(**color_kw)
PlotUtilities.color_y(**color_kw)