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 _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 _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 run():
input_dir = "../../../../Data/FECs180307/"
out_dir = "./"
q_offset_nm = 100
q_interp, energy_list_arr, _ = FigureUtil.\
_read_energy_list_and_q_interp(input_dir, q_offset=q_offset_nm,
min_fecs=9)
# read in some example data
base_dir_BR = input_dir + "BR+Retinal/"
names_BR = ["/BR+Retinal/3000nms/170503FEC/landscape_"]
PEG600 = FigureUtil._read_samples(base_dir_BR,names_BR)[0]
# read in the FECs
fecs = FigureUtil._snapsnot(PEG600.base_dir,
step=Pipeline.Step.REDUCED).fec_list
args_mean = (energy_list_arr, q_interp)
mean_A = mean_A_jarzynski(*args_mean)
mean_G = mean_G_iwt(*args_mean)
mean_F_from_dot = mean_A_dot_iwt(*args_mean)[0]
ex = fecs[0]
q = q_interp * 1e-9
# xxx offset q and z... probably a little off!
z = ex.ZFunc(ex) + q[0]
k = ex.SpringConstant
beta = ex.Beta
A = mean_A[0]
G = mean_G[0]
n_iters = 5000
force =True
kw_lr = dict(G0=G, A=A, q=q, z=z, k=k, beta=beta,n_iters=n_iters)
lr = CheckpointUtilities.getCheckpoint("./lr_deconv.pkl",
_deconvoled_lr,force,**kw_lr)
diff_kT = np.array(lr.mean_diffs) * beta
min_idx = np.argmin(diff_kT)
idx_search = np.logspace(start=3,stop=np.floor(np.log2(min_idx)),
endpoint=True,num=10,base=2)
idx_to_use = [int(i) for i in idx_search]
# fit a spline to the converged G to get the mean restoring force
fig = PlotUtilities.figure((4,6))
xlim = [min(q_interp)-5, max(q_interp)]
fmt_kw = dict(xlim=xlim,ylim=[None,None])
ax1 = plt.subplot(3,1,1)
plt.plot(diff_kT)
plt.axvline(min_idx)
FigureUtil._plot_fmt(ax1,is_bottom=True,xlabel="iter #",
ylabel="diff G (kT)",xlim=[None,None],ylim=[None,None])
ax2 = plt.subplot(3,1,2)
plt.plot(q_interp,lr._G0_initial_lr.G0_kT,color='b',linewidth=3)
plt.plot(q_interp,lr.G0_kT,color='r',linewidth=3)
FigureUtil._plot_fmt(ax2,is_bottom=False,ylabel="G (kT)",**fmt_kw)
ax1 = plt.subplot(3,1,3)
FigureUtil._plot_fec_list(fecs, xlim, ylim=[None,None])
for i in idx_to_use:
_plot_f_at_iter_idx(lr, i)
_plot_f_at_iter_idx(lr, 0,label="F from G0",linewidth=4)
plt.plot(q_interp,mean_F_from_dot*1e12,label="F from A_z_dot",linewidth=2)
# also plot the force we expect from the original A_z_dot
FigureUtil._plot_fmt(ax1,is_bottom=True,xlim=xlim,ylim=[None,None])
PlotUtilities.legend()
PlotUtilities.savefig(fig,"FigureS_A_z.png")
pass
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_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)