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 _G0_plot(plot_dir, data_sliced, landscape, fmt):
# XXX why is this necessary?? screwing up absolute values
previous_JCP = FigureUtil.read_non_peg_landscape(base="../../FigData/")
offset_s = np.mean([d.Separation[0] for d in data_sliced])
G_hao = landscape.G0_kcal_per_mol
idx_zero = np.where(landscape.q_nm <= 100)
G_hao = G_hao - landscape.G0_kcal_per_mol[0]
G_JCP = previous_JCP.G0_kcal_per_mol - previous_JCP.G0_kcal_per_mol[0] + 50
offset_jcp_nm = min(previous_JCP.q_nm)
landscape_offset_nm = min(landscape.q_nm)
q_JCP_nm = previous_JCP.q_nm - offset_jcp_nm + 5
q_Hao_nm = landscape.q_nm - landscape_offset_nm
fig = FigureUtil._fig_single(y=6)
xlim, ylim = FigureUtil._limits(data_sliced)
ax1 = plt.subplot(2, 1, 1)
FigureUtil._plot_fec_list(data_sliced, **fmt)
FigureUtil._plot_fmt(ax1, **fmt)
ax2 = plt.subplot(2, 1, 2)
plt.plot(q_Hao_nm, G_hao, label="Aligned, IWT")
plt.plot(q_JCP_nm, G_JCP, 'r--', label="JCP landscape")
FigureUtil._plot_fmt(ax2,
ylabel="G (kcal/mol)",
is_bottom=True,
xlim=xlim,
ylim=[None, None])
PlotUtilities.legend(ax=ax2, handlelength=2)
ax2.set_xlim(fmt['xlim'])
PlotUtilities.savefig(fig, plot_dir + "FigureSX_LandscapeComparison.png")
def _ensemble_alignment(gs, alignment, col_idx):
xlim, ylim = FigureUtil._limits(alignment._all_fecs)
common_kw = dict(xlim=xlim, ylim=ylim)
kw_fmt = dict(color=False, is_left=(col_idx == 0), **common_kw)
ax1 = plt.subplot(gs[0, col_idx])
FigureUtil._plot_fec_list(alignment.zeroed.fec_list, **common_kw)
FigureUtil._plot_fmt(ax1, **kw_fmt)
ax2 = plt.subplot(gs[1, col_idx])
FigureUtil._plot_fec_list(alignment.polished.fec_list, **common_kw)
FigureUtil._plot_fmt(ax2, **kw_fmt)
PlotUtilities.no_x_label(ax=ax2)
ax3 = plt.subplot(gs[2, col_idx])
FigureUtil._plot_fec_list(alignment.blacklisted.fec_list, **common_kw)
FigureUtil._plot_fmt(ax3, is_bottom=True, **kw_fmt)
def _make_plots(galleries_labels):
alignments = [
FigureUtil._alignment_pipeline(gallery_tmp[0])
for gallery_tmp in galleries_labels
]
for i, (_, label) in enumerate(galleries_labels):
# make the standard aligning plot
alignment = alignments[i]
_make_algned_plot(alignment, label)
# plot the final curves on the same plot
xlim, ylim = FigureUtil._limits(alignment._all_fecs)
colors = ['rebeccapurple', 'g']
fig = PlotUtilities.figure((5, 3))
gs = gridspec.GridSpec(2, 2)
# reverse everything, so PEG600 is on top
galleries_labels = galleries_labels[::-1]
alignments = alignments[::-1]
for i, (_, l) in enumerate(galleries_labels):
ax = plt.subplot(gs[i, 0])
a = alignments[i]
FigureUtil._plot_fec_list(a.blacklisted.fec_list,
xlim,
ylim,
label=l,
color=colors[i])
if (i == 0):
PlotUtilities.no_x_label(ax)
PlotUtilities.xlabel("", ax=ax)
# plot them both on the last column
plt.subplot(gs[:, 1])
kw = [dict(), dict(linewidth=0.6)]
for i, (_, l) in enumerate(galleries_labels):
a = alignments[i]
FigureUtil._plot_fec_list(a.blacklisted.fec_list,
xlim,
ylim,
label=l,
color=colors[i],
**kw[i])
PlotUtilities.savefig(fig, "FigureS_3400vs600.png")