gc = gc.drop(remove.index) #rcatn = R.rcatn #y = rcatn.median() # #fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12.5,6)) # fig = plt.figure() ax = plt.axes() cm = plt.cm.get_cmap('Blues') #conds = ['Chemostat u=0.11', 'Chemostat u=0.20', 'Chemostat u=0.31', 'Chemostat u=0.51'] #i = 0 for c in gc.index: try: y = R.kapp[c].dropna() # label = '$\mu=%.01f\,h^{-1}$'%x[c] cdf(y,color=cm(y.median()),ax=ax,lw=2.5) except: continue ax.set_xscale('log') ax.set_xlim(1e-4,1e2) ax.set_ylim(0,1) # ax1.plot([m, m], [0, 0.5], c=cm(x[c]*1.5/x.max()), ls='-') # props = dict(boxstyle='round', facecolor=cm(x[c]*0.8/x.max()), edgecolor='none') # ax1.text(m, 0.025+i, '%0.1f'%m, bbox=props, ha='center', size=fontsize/1.7) # i += 0.07 # #cdf(kcat, color='k', ax=ax1, label=r'$k_{\mathrm{cat}}$', lw=2.5) #ax1.plot([kcat.median(), kcat.median()], [0, 0.5], c='k', ls='-') #props = dict(boxstyle='round', facecolor='0.7', edgecolor='none') #ax1.text(kcat.median(), 0.02+i, '%0.1f'%kcat.median(), bbox=props, ha='center', size=fontsize/1.7)
kapp = R.kapp kmax = R.kmax['kmax per chain [s^-1]'] #kcat = R.kcat['kcat per chain [s^-1]'] kmax_usage = kapp.div(kmax, axis=0).dropna(how='all') kmax_usage = kmax_usage[gc.index & kmax_usage.columns] effective_capacity = pd.DataFrame(index=kmax_usage.index, columns=conditions) for reac in effective_capacity.index: r = R.rxns[reac] genes = map(lambda x: x.id, r.genes) try: effective_capacity.loc[reac] = mg_gCDW.loc[genes].sum() * kmax_usage.loc[reac] except: continue effective_capacity.dropna(how='all', inplace=True) glc_ec = effective_capacity['GLC_BATCH_mu=0.58_S'].dropna().astype(float) ace_ec = effective_capacity['ACE_BATCH_mu=0.3_S'].dropna().astype(float) from plot_types import cdf plt.figure() ax = plt.axes() cdf(glc_ec, ax=ax) cdf(ace_ec, ax=ax) ax.set_xscale('log') ax.set_xlim(1e-3,1e0) #kcat_usage = kapp.div(kmax, axis=0).dropna(how='any') #kcat_usage = kmax_usage[gc.index & kmax_usage.columns]