# now make the plot
site_seq = plottingutils.getwtseq(infofn[namedict[fn]],barcodefn,datafnbase)
fig = plt.figure()
ax1 = SubplotHost(fig, 1,1,1)
fig.add_subplot(ax1)
ax2 = ax1.twin()
ax1.imshow(emat,interpolation='nearest')
ax1.set_xlabel('Position w.r.t. transcription start site')
ax1.set_yticks([0,1,2,3])
ax1.set_yticklabels(['A','C','G','T'])
# label positions with respect to transcription start site
tick_start = int(start_dict[info_dict['exp_name']])+int(info_dict['mut_region_start'])
tick_end = int(start_dict[info_dict['exp_name']])+int(info_dict['mut_region_start']) + int(info_dict['mut_region_length'])
indices, xtick_labels = clean_up_xticklabels(tick_start,tick_end-tick_start)
ax1.set_xticks(indices)
ax1.set_xticklabels(xtick_labels)
# put the sequence above it
ax2.set_yticks([])
ax2.set_yticklabels([])
ax2.set_xticks(range(20))
ax2.set_xticklabels([bp for bp in site_seq])
#plt.title('$lac$ promoter, -35 region')
emat = emat0 - emat0.min(axis=0)
# now make the plot
site_seq = plottingutils.getwtseq(infofn[namedict[fn]], barcodefn,
datafnbase)
fig = plt.figure()
ax1 = SubplotHost(fig, 1, 1, 1)
fig.add_subplot(ax1)
ax2 = ax1.twin()
ax1.imshow(emat, interpolation='nearest')
ax1.set_xlabel('Position w.r.t. transcription start site')
ax1.set_yticks([0, 1, 2, 3])
ax1.set_yticklabels(['A', 'C', 'G', 'T'])
# label positions with respect to transcription start site
tick_start = int(start_dict[info_dict['exp_name']]) + int(
info_dict['mut_region_start'])
tick_end = int(start_dict[info_dict['exp_name']]) + int(
info_dict['mut_region_start']) + int(
info_dict['mut_region_length'])
indices, xtick_labels = clean_up_xticklabels(tick_start,
tick_end - tick_start)
ax1.set_xticks(indices)
ax1.set_xticklabels(xtick_labels)
# put the sequence above it
ax2.set_yticks([])
ax2.set_yticklabels([])
bar2 = (stressSod[1], stressSod[0]-stressSod[1])
ax1.broken_barh([bar1, bar2], (31, 4),
facecolors=(fillSod, fill),
edgecolor=edgeSod,)
for i in range(len(youngs)):
ax1.annotate('${0:g}$'.format(youngs[i]*1e-9), \
xy=(stressSod[i], 36), \
xycoords='data', horizontalalignment=alignRev[i],
fontsize=fs)
#ax1.set_title('\\textbf{(b)} Molten salt')
ax1.set_ylim(0, 40)
ax1.set_yticks([5, 15, 25, 35])
ax1.set_yticklabels(
['$\\mathrm{DN}$\n\small{(-)}',
'$\\lambda$\n\small{(\si{\watt\per\meter\per\kelvin})}',
'$\\alpha$\n\small{(\SI{e-6}{\per\kelvin})}',
'$E$\n\small{(GPa)}'], fontsize='large'
)
ax1.set_xlim(150, 450)
ax1.set_xlabel(r'\textsc{max. equiv. stress}, '+\
'$\max\sigma_\mathrm{Eq}$ (MPa)')
fig1.tight_layout()
#plt.show()
fig1.savefig('S31609_sensitivityTubeProperties.pdf',
transparent=True)
plt.close(fig1)
headerprint(' Fluid flow parameter variation ')
fig2 = plt.figure(figsize=(5, 2.5))
