def fix_axes(ax_list):
# loop through all the axes and toss them.
xlims = get_ranges(ax_list, get_x=True)
ylims = get_ranges(ax_list, get_x=False)
for i, axs in enumerate(ax_list):
for j, ax in enumerate(axs):
if (i > 0):
# columns after the first lose their labels
PlotUtilities.no_y_label(ax)
PlotUtilities.ylabel("", ax=ax)
ax.set_ylim(ylims[j])
if (j != 0):
PlotUtilities.no_x_label(ax)
PlotUtilities.xlabel("", ax=ax)
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)
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
default_base = "../../../Data/170321FEC/"
base_dir = Pipeline._base_dir_from_cmd(default=default_base)
step = Pipeline.Step.POLISH
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.ALIGNED)
out_dir = Pipeline._cache_dir(base=base_dir,enum=step)
plot_dir = Pipeline._plot_subdir(base=base_dir, enum=step)
force = True
limit = None
functor = lambda : polish_data(in_dir)
data =CheckpointUtilities.multi_load(cache_dir=out_dir,load_func=functor,
force=force,
limit=limit,
name_func=FEC_Util.fec_name_func)
sizes = [d.Force.size for d in data]
min_s = min(sizes)
sliced_data = [d._slice(slice(0,min_s,1)) for d in data]
for d in sliced_data:
d.Offset = d.ZSnsr[0]
d.Extension = d.Separation
d.kT = 4.1e-21
data_wham = UtilWHAM.to_wham_input(objs=sliced_data, n_ext_bins=75)
data_wham.z = np.array([d.ZSnsr for d in sliced_data])
obj_wham = WeightedHistogram.wham(data_wham)
data_unpolished = CheckpointUtilities.lazy_multi_load(in_dir)
f_x_zsnsr = lambda x: x.ZSnsr
xlim = [-40,150]
ProcessingUtil.heatmap_ensemble_plot(data,out_name=plot_dir + "heatmap.png",
xlim=xlim)
ProcessingUtil.heatmap_ensemble_plot(data,f_x=f_x_zsnsr,
out_name=plot_dir + "heatmap_Z.png",
kw_map=dict(x_func=f_x_zsnsr),
xlim=xlim)
# plot each individual
f_x = lambda x_tmp : x_tmp.Separation
plot_subdir = Pipeline._plot_subdir(base_dir, step)
name_func = FEC_Util.fec_name_func
_, ylim = ProcessingUtil.nm_and_pN_limits(data,f_x)
for i,(d_unpolish,d_polish) in enumerate(zip(data_unpolished,data)):
kw_plot = dict(d_unpolish=d_unpolish,
d_polish=d_polish,
xlim=xlim,ylim=ylim,i=i)
fig = PlotUtilities.figure((6,6))
# make the Separation column
ax1,ax2 = plt.subplot(2,2,1), plt.subplot(2,2,3)
polish_plot(ax1, ax2, f_x = lambda x: x.Separation,
plot_components_1=True,**kw_plot)
# make the ZSnsr column
ax3,ax4 = plt.subplot(2,2,2), plt.subplot(2,2,4)
polish_plot(ax3, ax4, f_x = lambda x: x.ZSnsr,plot_components_1=False,
**kw_plot)
PlotUtilities.xlabel("Stage Position (nm)", ax=ax4)
for a in [ax3,ax4]:
PlotUtilities.no_y_label(ax=a)
PlotUtilities.ylabel("",ax=a)
name = plot_subdir + name_func(0, d_polish) + ".png"
PlotUtilities.savefig(fig,name)
def make_comparison_plot(q_interp,
energy_list_arr,
G_no_peg,
add_annotations,
limit_plot=None):
landscpes_with_error = \
FigureUtil._get_error_landscapes(q_interp, energy_list_arr)
# get the extension grid we wnt...
ext_grid = np.linspace(0, 25, num=100)
# read in Hao's energy landscape
fec_system = WLC._make_plot_inf(ext_grid, WLC.read_haos_data)
shifts = [fec_system.W_at_f(f) for f in [249, 149]]
gs = gridspec.GridSpec(nrows=1, ncols=1)
ax1, means, stdevs = \
make_retinal_subplot(gs,landscpes_with_error,shifts,
limit_plot=limit_plot)
# get the with-retinal max
ax1 = plt.subplot(gs[0])
# get the max of the last point (the retinal energy landscape is greater)
offsets = [l.G0_kcal_per_mol[-1] for l in landscpes_with_error]
q_no_PEG_start = max(q_interp)
q_nm_no_PEG = G_no_peg.q_nm + q_no_PEG_start
G_err_kcal_no_PEG = G_no_peg.G_err_kcal
for i, G_offset in enumerate(offsets[:limit_plot]):
G_kcal = G_no_peg.G0_kcal_per_mol + G_offset
mean_err = np.mean(G_err_kcal_no_PEG)
idx_errorbar = q_nm_no_PEG.size // 2
common_style = dict(color='grey', linewidth=1.5)
ax1.plot(q_nm_no_PEG, G_kcal, linestyle='--', **common_style)
if (i != 0):
continue
ax1.errorbar(q_nm_no_PEG[idx_errorbar],
G_kcal[idx_errorbar],
yerr=mean_err,
marker=None,
markersize=0,
capsize=3,
**common_style)
axes = [ax1]
ylim = [None,
np.max(offsets) + max(G_no_peg.G0_kcal_per_mol) + \
G_err_kcal_no_PEG[-1]*4]
for a in axes:
a.set_ylim(ylim)
a.set_xlim([None, max(q_nm_no_PEG)])
# add in the scale bar
ax = axes[0]
xlim = ax.get_xlim()
ylim = ax.get_ylim()
y_range = (ylim[1] - ylim[0])
range_scale_kcal = np.round(y_range / 3, -2)
x_range_scalebar_nm = 20
min_offset, _, rel_delta = Scalebar. \
offsets_zero_tick(limits=ylim,range_scalebar=range_scale_kcal)
min_offset_x, _, rel_delta_x= Scalebar. \
offsets_zero_tick(limits=xlim,range_scalebar=x_range_scalebar_nm)
offset_x = 0.25
offset_y = min_offset + 1 * rel_delta
common_kw = dict(add_minor=True)
scalebar_kw = dict(offset_x=offset_x,
offset_y=offset_y,
ax=ax,
x_on_top=True,
y_on_right=False,
x_kwargs=dict(width=x_range_scalebar_nm,
unit="nm",
**common_kw),
y_kwargs=dict(height=range_scale_kcal,
unit="kcal/mol",
**common_kw))
PlotUtilities.no_x_label(ax=ax)
PlotUtilities.no_y_label(ax=ax)
Scalebar.crossed_x_and_y_relative(**scalebar_kw)
# add the helical boxes
offset_boxes_nm = -25
FigureUtil.add_helical_boxes(ax=ax1,
ymax_box=0.97,
box_height=0.07,
constant_offset=offset_boxes_nm,
clip_on=True)
delta_delta_G_total = np.abs(np.diff(offsets))[0]
delta_delta_G_linker = np.abs(np.diff(shifts))[0]
if add_annotations:
str_text = " $\mathbf{\Delta\Delta}G_{\mathbf{Total}}$"
min_o = min(offsets)
xy = q_no_PEG_start, min_o
xy_end = q_no_PEG_start, min_o + delta_delta_G_total
labelled_arrow(ax1, str_text, xy, xy_end)
str_dd = " $\mathbf{\Delta\Delta}G_{\mathbf{Linker}}$"
xlim = plt.xlim()
x_range = (xlim[1] - xlim[0])
x0 = np.mean(xlim) + x_range * 0.05
y0 = min_o
y1 = min_o + delta_delta_G_linker
xy2 = x0, y0
xy_end2 = x0, y1
labelled_arrow(ax1,
str_dd,
xy2,
xy_end2,
color_arrow='r',
y_text_fudge=0.0)
# # save out the data
preamble = "FigureS6"
X = np.array([delta_delta_G_total, delta_delta_G_linker]).reshape((1, -1))
np.savetxt(fname=preamble + "_ddG.csv",
X=X,
fmt=["%.1f", "%.1f"],
delimiter=",",
header="ddG_total (kcal/mol), ddG_linker (kcal/mol)")
# save out the landscapes
x_y_yerr_name = [[q_interp, landscpes_with_error[0], preamble + "_BR"],
[q_interp, landscpes_with_error[1], preamble + "_BO"]]
for x, l, name in x_y_yerr_name:
y = l.G0_kcal_per_mol
yerr = l.G_err_kcal
Record.save_csv(
dict(x=x,
y=[y, yerr],
save_name=name,
x_name="q",
x_units="nm",
y_name=["Energy", "Energy Error"],
y_units="kcal/mol"))
# save out the remainder of the BO
G_kcal_PEG_tmp = G_no_peg.G0_kcal_per_mol + max(offsets)
Record.save_csv(
dict(x=q_nm_no_PEG,
y=[G_kcal_PEG_tmp, G_err_kcal_no_PEG],
save_name=preamble + "_JCP_no_PEG",
x_name="q",
x_units="nm",
y_name=["Energy", "Energy Error"],
y_units="kcal/mol"))