# 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))