def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
model, descr = WLC.Hao_PEGModel, "Hao_Parameters",
fig = PlotUtilities.figure((4, 6))
plot_inf = WLC._make_plot_inf(ext_grid=np.linspace(0, 30, num=3000),
read_functor=WLC.read_haos_data,
base="../../FigData/")
make_model_plot(plot_inf=plot_inf, title=descr)
PlotUtilities.savefig(fig, "PEG_{:s}.png".format(descr))
# make sure what we have matches Hao.
fig = PlotUtilities.figure((2.5, 4))
make_comparison_plot()
plt.xlim([0, 35])
plt.ylim([0, 300])
PlotUtilities.legend(frameon=True)
PlotUtilities.savefig(fig, "out_compare.png")
pass
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 _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 _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 make_retinal_subplot(gs,
energy_list_arr,
shifts,
skip_arrow=True,
limit_plot=None):
q_interp_nm = energy_list_arr[0].q_nm
means = [e.G0_kcal_per_mol for e in energy_list_arr]
# fit a second order polynomial and subtract from each point
q_fit_nm_relative = 7
max_fit_idx = \
np.argmin(np.abs((q_interp_nm - q_interp_nm[0]) - q_fit_nm_relative))
fits = []
fit_pred_arr = []
for m in means:
fit = np.polyfit(x=q_interp_nm[:max_fit_idx], y=m[:max_fit_idx], deg=2)
fits.append(fit)
fit_pred = np.polyval(fit, x=q_interp_nm)
fit_pred_arr.append(fit_pred)
stdevs = [e.G_err_kcal for e in energy_list_arr]
ax1 = plt.subplot(gs[0])
common_error = dict(capsize=0)
style_dicts = [
dict(color=FigureUtil.color_BR(), label=r"with retinal"),
dict(color=FigureUtil.color_BO(), label=r"w/o retinal")
]
markers = ['v', 'x']
deltas, deltas_std = [], []
delta_styles = [
dict(color=style_dicts[i]['color'],
markersize=5,
linestyle='None',
marker=markers[i],
**common_error) for i in range(len(energy_list_arr))
]
xlim = [None, 27]
ylim = [-25, 450]
q_arr = []
round_energy = -1
max_q_nm = max(q_interp_nm)
# add the 'shifted' energies
for i, (mean,
stdev) in enumerate(zip(means[:limit_plot], stdevs[:limit_plot])):
tmp_style = style_dicts[i]
style_fit = dict(**tmp_style)
style_fit['linestyle'] = '--'
style_fit['label'] = None
corrected = mean - fit_pred_arr[i]
plt.plot(q_interp_nm, mean, **tmp_style)
plt.fill_between(x=q_interp_nm,
y1=mean - stdev,
y2=mean + stdev,
color=tmp_style['color'],
linewidth=0,
alpha=0.3)
energy_error = np.mean(stdev)
max_idx = -1
q_at_max_energy = q_interp_nm[max_idx]
max_energy_mean = corrected[max_idx]
# for the error, use the mean error over all interpolation
max_energy_std = energy_error
deltas.append(max_energy_mean)
deltas_std.append(max_energy_std)
q_arr.append(q_at_max_energy)
plt.xlim(xlim)
plt.ylim(ylim)
PlotUtilities.lazyLabel("Extension (nm)", "$\mathbf{\Delta}G$ (kcal/mol)",
"")
leg = PlotUtilities.legend(loc='lower right')
colors_leg = [s['color'] for s in style_dicts]
PlotUtilities.color_legend_items(leg, colors=colors_leg[:limit_plot])
return ax1, means, stdevs