def find_path(self, experiment_name, net_reaction):
"""Find a pathway from the source to the target.
Args:
experiment_name: a name given to this experiment.
net_reaction: a Reaction describing the net reaction for the desired paths
"""
dirname = os.path.join('../res/pathologic/', experiment_name)
logging.info('Writing output to: %s' % dirname)
util._mkdir(dirname)
self.html_writer.write('<a href="pathologic/' + experiment_name + '.html">' + experiment_name + '</a><br>\n')
exp_html = HtmlWriter('../res/pathologic/' + experiment_name + '.html')
exp_html.write("<p><h1>%s</h1>\n" % experiment_name)
exp_html.insert_toggle(div_id="__parameters__", start_here=True,
label='Show Parameters')
f, S, compounds, reactions = self.kegg_pathologic.get_unique_cids_and_reactions()
exp_html.write('<h2>Conditions:</h2>\n')
exp_html.write_ul(['Optimization method: %s' % self.thermodynamic_method,
'Concentration range: %g M < C < %g M' % (self.thermo.c_range[0], self.thermo.c_range[1]),
"Max Δ<sub>r</sub>G' = %.1f" % self.maximal_dG,
'pH = %g' % self.thermo.pH,
'I = %g' % self.thermo.I,
'T = %g' % self.thermo.T,
'Max no. reactions: %d' % (self.max_reactions or -1),
'Max no. solutions: %d' % (self.max_solutions or -1),
'Overall Reaction: %s' % net_reaction.to_hypertext(),
'%d reactions' % len(reactions),
'%d unique compounds' % len(compounds)])
exp_html.div_end()
exp_html.write('</br>\n')
logging.debug("All compounds:")
for i, compound in enumerate(compounds):
logging.debug("%05d) C%05d = %s" % (i, compound.cid, compound.name))
logging.debug("All reactions:")
for i, reaction in enumerate(reactions):
logging.debug("%05d) R%05d = %s" % (i, reaction.rid, str(reaction)))
output_kegg_file = open(dirname + '/kegg_pathway.txt', 'w')
exp_html.write('<a href="%s/kegg_pathway.txt">All solutions in KEGG format</a></br>\n'
% experiment_name)
# Find a solution with a minimal total flux
logging.info("Preparing LP solver for the minimal total flux problem")
exp_html.write('<b>Minimum flux</b>')
slip = Stoichiometric_LP("Pathologic")
slip.add_stoichiometric_constraints(f, S, compounds, reactions, net_reaction)
slip.export("../res/pathologic/%s/%03d_lp.txt" % (experiment_name, 0))
exp_html.write(' (<a href="%s/%03d_lp.txt">LP file</a>): ' % (experiment_name, 0))
logging.info("Solving")
if not slip.solve():
exp_html.write("<b>There are no solutions!</b>")
logging.warning("There are no solutions. Quitting!")
return
logging.info("writing solution")
self.write_current_solution(exp_html, slip, experiment_name)
logging.info("Preparing MILP solver")
milp = Stoichiometric_LP("Pathologic")
milp.solution_index = 1
milp.add_stoichiometric_constraints(f, S, compounds, reactions, net_reaction)
milp.add_milp_variables()
if self.max_reactions is not None:
milp.add_reaction_num_constraint(self.max_reactions)
if self.thermodynamic_method == OptimizationMethods.LOCALIZED:
milp.add_localized_dGf_constraints(self.thermo)
else:
milp.add_dGr_constraints(self.thermo,
optimization=self.thermodynamic_method,
maximal_dG=self.maximal_dG)
index = 0
while (self.max_solutions is None) or (index < self.max_solutions):
index += 1
# create the MILP problem to constrain the previous solutions not to reappear again.
logging.info("Round %03d, solving using MILP" % (milp.solution_index))
milp.export("../res/pathologic/%s/%03d_lp.txt" % (experiment_name, milp.solution_index))
exp_html.write('<b>Solution #%d</b> (<a href="%s/%03d_lp.txt">LP file</a>): ' % (index, experiment_name, index))
if not milp.solve():
exp_html.write("<b>No solution found</b>")
logging.info("No more solutions. Quitting!")
break
logging.info("writing solution")
self.write_current_solution(exp_html, milp, experiment_name,
output_kegg_file)
milp.ban_current_solution()
output_kegg_file.close()
exp_html.close()
def analyze(prefix, thermo):
kegg_file = ParsedKeggFile.FromKeggFile('../data/thermodynamics/%s.txt' % prefix)
html_writer = HtmlWriter('../res/%s.html' % prefix)
co2_hydration = Reaction.FromFormula("C00011 + C00001 => C00288")
#pH_vec = np.arange(5, 9.001, 0.5)
#pH_vec = np.array([6, 7, 8])
pH_vec = np.array([6, 7, 8]) # this needs to be fixed so that the txt file will set the pH
#co2_conc_vec = np.array([1e-5, 1e-3])
co2_conc_vec = np.array([1e-5])
data_mat = []
override_bounds = {}
for pH in pH_vec.flat:
co2_hydration_dG0_prime = float(thermo.GetTransfromedKeggReactionEnergies([co2_hydration], pH=pH))
for co2_conc in co2_conc_vec.flat:
carbonate_conc = co2_conc * np.exp(-co2_hydration_dG0_prime / (R*default_T))
#print "[CO2] = %g, [carbonate] = %g, pH = %.1f, I = %.2fM" % (co2_conc, carbonate_conc, pH, I)
override_bounds[11] = (co2_conc, co2_conc)
override_bounds[288] = (carbonate_conc, carbonate_conc)
section_prefix = 'pH_%g_CO2_%g' % (pH, co2_conc*1000)
section_title = 'pH = %g, [CO2] = %g mM' % (pH, co2_conc*1000)
html_writer.write('<h1 id="%s_title">%s</h1>\n' %
(section_prefix, section_title))
html_writer.write_ul(['<a href="#%s_tables">Individual result tables</a>' % section_prefix,
'<a href="#%s_summary">Summary table</a>' % section_prefix,
'<a href="#%s_figure">Summary figure</a>' % section_prefix])
data, labels = pareto(kegg_file, html_writer, thermo,
pH=pH, section_prefix=section_prefix, balance_water=True,
override_bounds=override_bounds)
data_mat.append(data)
data_mat = np.array(data_mat)
if data_mat.shape[0] == 1:
pareto_fig = plt.figure(figsize=(6, 6), dpi=90)
plt.plot(data_mat[0, :, 0], data_mat[0, :, 1], '.', figure=pareto_fig)
for i in xrange(data_mat.shape[1]):
if data[i, 1] < 0:
color = 'grey'
else:
color = 'black'
plt.text(data_mat[0, i, 0], data_mat[0, i, 1], labels[i],
ha='left', va='bottom',
fontsize=8, color=color, figure=pareto_fig)
plt.title(section_title, figure=pareto_fig)
else:
pareto_fig = plt.figure(figsize=(10, 10), dpi=90)
for i in xrange(data_mat.shape[1]):
plt.plot(data_mat[:, i, 0], data_mat[:, i, 1], '-', figure=pareto_fig)
plt.text(data_mat[0, i, 0], data_mat[0, i, 1], '%g' % pH_vec[0],
ha='center', fontsize=6, color='black', figure=pareto_fig)
plt.text(data_mat[-1, i, 0], data_mat[-1, i, 1], '%g' % pH_vec[-1],
ha='center', fontsize=6, color='black', figure=pareto_fig)
plt.legend(labels, loc='upper right')
plt.title('Pareto', figure=pareto_fig)
plt.xlabel('Optimal Energetic Efficiency [kJ/mol]', figure=pareto_fig)
plt.ylabel('Optimized Distributed Bottleneck [kJ/mol]', figure=pareto_fig)
html_writer.write('<h2 id="%s_figure">Summary figure</h1>\n' % section_prefix)
# plot the Pareto figure showing all values (including infeasible)
html_writer.embed_matplotlib_figure(pareto_fig, name=prefix + '_0')
# set axes to hide infeasible pathways and focus on feasible ones
pareto_fig.axes[0].set_xlim(None, 0)
pareto_fig.axes[0].set_ylim(0, None)
html_writer.embed_matplotlib_figure(pareto_fig, name=prefix + '_1')
html_writer.close()
def analyze(prefix, thermo):
kegg_file = ParsedKeggFile.FromKeggFile('../data/thermodynamics/%s.txt' %
prefix)
html_writer = HtmlWriter('../res/%s.html' % prefix)
co2_hydration = Reaction.FromFormula("C00011 + C00001 => C00288")
#pH_vec = np.arange(5, 9.001, 0.5)
#pH_vec = np.array([6, 7, 8])
pH_vec = np.array(
[6, 7,
8]) # this needs to be fixed so that the txt file will set the pH
#co2_conc_vec = np.array([1e-5, 1e-3])
co2_conc_vec = np.array([1e-5])
data_mat = []
override_bounds = {}
for pH in pH_vec.flat:
co2_hydration_dG0_prime = float(
thermo.GetTransfromedKeggReactionEnergies([co2_hydration], pH=pH))
for co2_conc in co2_conc_vec.flat:
carbonate_conc = co2_conc * np.exp(-co2_hydration_dG0_prime /
(R * default_T))
#print "[CO2] = %g, [carbonate] = %g, pH = %.1f, I = %.2fM" % (co2_conc, carbonate_conc, pH, I)
override_bounds[11] = (co2_conc, co2_conc)
override_bounds[288] = (carbonate_conc, carbonate_conc)
section_prefix = 'pH_%g_CO2_%g' % (pH, co2_conc * 1000)
section_title = 'pH = %g, [CO2] = %g mM' % (pH, co2_conc * 1000)
html_writer.write('<h1 id="%s_title">%s</h1>\n' %
(section_prefix, section_title))
html_writer.write_ul([
'<a href="#%s_tables">Individual result tables</a>' %
section_prefix,
'<a href="#%s_summary">Summary table</a>' % section_prefix,
'<a href="#%s_figure">Summary figure</a>' % section_prefix
])
data, labels = pareto(kegg_file,
html_writer,
thermo,
pH=pH,
section_prefix=section_prefix,
balance_water=True,
override_bounds=override_bounds)
data_mat.append(data)
data_mat = np.array(data_mat)
if data_mat.shape[0] == 1:
pareto_fig = plt.figure(figsize=(6, 6), dpi=90)
plt.plot(data_mat[0, :, 0], data_mat[0, :, 1], '.', figure=pareto_fig)
for i in xrange(data_mat.shape[1]):
if data[i, 1] < 0:
color = 'grey'
else:
color = 'black'
plt.text(data_mat[0, i, 0],
data_mat[0, i, 1],
labels[i],
ha='left',
va='bottom',
fontsize=8,
color=color,
figure=pareto_fig)
plt.title(section_title, figure=pareto_fig)
else:
pareto_fig = plt.figure(figsize=(10, 10), dpi=90)
for i in xrange(data_mat.shape[1]):
plt.plot(data_mat[:, i, 0],
data_mat[:, i, 1],
'-',
figure=pareto_fig)
plt.text(data_mat[0, i, 0],
data_mat[0, i, 1],
'%g' % pH_vec[0],
ha='center',
fontsize=6,
color='black',
figure=pareto_fig)
plt.text(data_mat[-1, i, 0],
data_mat[-1, i, 1],
'%g' % pH_vec[-1],
ha='center',
fontsize=6,
color='black',
figure=pareto_fig)
plt.legend(labels, loc='upper right')
plt.title('Pareto', figure=pareto_fig)
plt.xlabel('Optimal Energetic Efficiency [kJ/mol]', figure=pareto_fig)
plt.ylabel('Optimized Distributed Bottleneck [kJ/mol]', figure=pareto_fig)
html_writer.write('<h2 id="%s_figure">Summary figure</h1>\n' %
section_prefix)
# plot the Pareto figure showing all values (including infeasible)
html_writer.embed_matplotlib_figure(pareto_fig, name=prefix + '_0')
# set axes to hide infeasible pathways and focus on feasible ones
pareto_fig.axes[0].set_xlim(None, 0)
pareto_fig.axes[0].set_ylim(0, None)
html_writer.embed_matplotlib_figure(pareto_fig, name=prefix + '_1')
html_writer.close()
