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]
