def main():
options, _ = MakeOpts().parse_args(sys.argv)
db = SqliteDatabase("../res/gibbs.sqlite")
public_db = SqliteDatabase("../data/public_data.sqlite")
output_filename = os.path.abspath(options.output_filename)
logging.info('Will write output to %s' % output_filename)
html_writer = HtmlWriter(output_filename)
nist = Nist(T_range=None)
nist_regression = NistRegression(db, html_writer=html_writer, nist=nist)
nist_regression.std_diff_threshold = 5 # the threshold over which to print an analysis of a reaction
#nist_regression.nist.T_range = None(273.15 + 24, 273.15 + 40)
#nist_regression.nist.override_I = 0.25
#nist_regression.nist.override_pMg = 14.0
html_writer.write("<h2>NIST regression:</h2>")
if options.use_prior:
logging.info('Using the data from Alberty as fixed prior')
prior_thermo = PsuedoisomerTableThermodynamics.FromDatabase(
public_db, 'alberty_pseudoisomers', name="Alberty")
else:
prior_thermo = None
html_writer.write('</br><b>Regression Tables</b>\n')
html_writer.insert_toggle(start_here=True)
nist_regression.Train(options.from_database, prior_thermo)
html_writer.div_end()
html_writer.write('</br><b>PRC results</b>\n')
html_writer.insert_toggle(start_here=True)
nist_regression.WriteDataToHtml(html_writer)
html_writer.div_end()
html_writer.write('</br><b>Transformed reaction energies - PRC vs. Observed</b>\n')
html_writer.insert_toggle(start_here=True)
N, rmse = nist_regression.VerifyResults()
html_writer.div_end()
logging.info("Regression results for transformed data:")
logging.info("N = %d, RMSE = %.1f" % (N, rmse))
html_writer.close()
def main():
html_writer = HtmlWriter("../res/nist/report.html")
estimators = LoadAllEstimators()
nist = Nist()
nist.T_range = (273.15 + 24, 273.15 + 40)
#nist.override_I = 0.25
#nist.override_pMg = 14.0
#nist.override_T = 298.15
html_writer.write('<p>\n')
html_writer.write("Total number of reaction in NIST: %d</br>\n" %
len(nist.data))
html_writer.write("Total number of reaction in range %.1fK < T < %.1fK: %d</br>\n" % \
(nist.T_range[0], nist.T_range[1], len(nist.SelectRowsFromNist())))
html_writer.write('</p>\n')
reactions = {}
reactions['KEGG'] = []
for reaction in Kegg.getInstance().AllReactions():
try:
reaction.Balance(balance_water=True, exception_if_unknown=True)
reactions['KEGG'].append(reaction)
except (KeggReactionNotBalancedException, KeggParseException,
OpenBabelError):
pass
reactions['FEIST'] = Feist.FromFiles().reactions
reactions['NIST'] = nist.GetUniqueReactionSet()
pairs = []
#pairs += [('hatzi_gc', 'UGC')], ('PGC', 'PRC'), ('alberty', 'PRC')]
for t1, t2 in pairs:
logging.info('Writing the NIST report for %s vs. %s' %
(estimators[t1].name, estimators[t2].name))
html_writer.write('<p><b>%s vs. %s</b> ' %
(estimators[t1].name, estimators[t2].name))
html_writer.insert_toggle(start_here=True)
two_way_comparison(html_writer=html_writer,
thermo1=estimators[t1],
thermo2=estimators[t2],
reaction_list=reactions['FEIST'],
name='%s_vs_%s' % (t1, t2))
html_writer.div_end()
html_writer.write('</p>')
if False:
estimators['alberty'].CompareOverKegg(
html_writer,
other=estimators['PRC'],
fig_name='kegg_compare_alberty_vs_nist')
rowdicts = []
rowdict = {'Method': 'Total'}
for db_name, reaction_list in reactions.iteritems():
rowdict[db_name + ' coverage'] = len(reaction_list)
rowdicts.append(rowdict)
for name in ['UGC', 'PGC', 'PRC', 'alberty', 'merged', 'hatzi_gc']:
thermo = estimators[name]
logging.info('Writing the NIST report for %s' % thermo.name)
html_writer.write('<p><b>%s</b> ' % thermo.name)
html_writer.insert_toggle(start_here=True)
num_estimations, rmse = nist.verify_results(html_writer=html_writer,
thermodynamics=thermo,
name=name)
html_writer.div_end()
html_writer.write('N = %d, RMSE = %.1f</p>\n' %
(num_estimations, rmse))
logging.info('N = %d, RMSE = %.1f' % (num_estimations, rmse))
rowdict = {
'Method': thermo.name,
'RMSE (kJ/mol)': "%.1f (N=%d)" % (rmse, num_estimations)
}
for db_name, reaction_list in reactions.iteritems():
n_covered = thermo.CalculateCoverage(reaction_list)
percent = n_covered * 100.0 / len(reaction_list)
rowdict[db_name +
" coverage"] = "%.1f%% (%d)" % (percent, n_covered)
logging.info(db_name + " coverage = %.1f%%" % percent)
rowdicts.append(rowdict)
headers = ['Method', 'RMSE (kJ/mol)'] + \
[db_name + ' coverage' for db_name in reactions.keys()]
html_writer.write_table(rowdicts, headers=headers)
def main():
kegg = Kegg.getInstance()
prefix = "../res/prc_"
fixed_cids = {} # a dictionary from CID to pairs of (nH, dG0)
# Alberty formation energies directly measured, linearly independent:
fixed_cids[1] = (2, -237.19) # H2O
fixed_cids[9] = (1, -1096.1) # HPO3(-2)
fixed_cids[14] = (4, -79.31) # NH4(+1)
fixed_cids[59] = (0, -744.53) # SO4(-2)
fixed_cids[288] = (1, -586.77) # HCO3(-1)
# Alberty zeros:
fixed_cids[3] = (26, 0.0) # NAD(ox)
fixed_cids[10] = (32, 0.0) # CoA
fixed_cids[127] = (30, 0.0) # glutathione(ox)
fixed_cids[376] = (28, 0.0) # retinal(ox)
# Directly measured values
fixed_cids[4] = (27, 22.65) # NAD(red) -- relative to NAD(ox)
fixed_cids[212] = (13, -194.5) # adenosine
# fixed_cids[294] = (12, -409.2) # inosine - linearly dependent on other 'anchors'
# Alberty zeros which are not in NIST:
# fixed_cids[524] = ( 0, 0.0) # cytochrome c(ox)
# fixed_cids[16] = (31, 0.0) # FAD(ox)
# fixed_cids[139] = ( 0, 0.0) # ferredoxin(ox)
# fixed_cids[61] = (19, 0.0) # FMN(ox)
# fixed_cids[343] = ( 0, 0.0) # thioredoxin(ox)
# fixed_cids[399] = (90, 0.0) # ubiquinone(ox)
public_db = SqliteDatabase("../data/public_data.sqlite")
alberty = PsuedoisomerTableThermodynamics.FromDatabase(
public_db, "alberty_pseudoisomers", label=None, name="Alberty"
)
alberty_cid2dG0 = {}
alberty_cid2nH = {}
for cid in alberty.get_all_cids():
pmap = alberty.cid2PseudoisomerMap(cid)
dG0, _dG0_tag, nH, _z, _nMg = pmap.GetMostAbundantPseudoisomer(
pH=default_pH, I=default_I, pMg=default_pMg, T=default_T
)
alberty_cid2nH[cid] = nH
alberty_cid2dG0[cid] = dG0
if not os.path.exists(prefix + "S.txt"):
db = SqliteDatabase("../res/gibbs.sqlite")
nist_regression = NistRegression(db)
cid2nH = {}
for cid in nist_regression.nist.GetAllCids():
if cid in fixed_cids:
cid2nH[cid] = fixed_cids[cid][0]
elif cid in alberty_cid2nH:
cid2nH[cid] = alberty_cid2nH[cid]
else:
tmp = nist_regression.dissociation.GetMostAbundantPseudoisomer(
cid, pH=default_pH, I=default_I, pMg=default_pMg, T=default_T
)
if tmp is not None:
cid2nH[cid] = tmp[0]
else:
logging.warning(
"The most abundant pseudoisomer of %s (C%05d) "
"cannot be resolved. Using nH = 0." % (kegg.cid2name(cid), cid)
)
cid2nH[cid] = 0
# nist_regression.std_diff_threshold = 2.0 # the threshold over which to print an analysis of a reaction
# nist_regression.nist.T_range = None#(273.15 + 24, 273.15 + 40)
S, dG0, cids = nist_regression.ReverseTransform(cid2nH=cid2nH)
# export the raw data matrices to text files
C = np.array([[cid, cid2nH.get(cid, 0)] for cid in cids])
np.savetxt(prefix + "CID.txt", C, fmt="%d", delimiter=",")
np.savetxt(prefix + "S.txt", S, fmt="%g", delimiter=",")
np.savetxt(prefix + "dG0.txt", dG0, fmt="%.2f", delimiter=",")
else:
C = np.loadtxt(prefix + "CID.txt", delimiter=",")
cids = [int(cid) for cid in C[:, 0]]
cid2nH = {}
for i, cid in enumerate(cids):
cid2nH[cid] = int(C[i, 1])
S = np.loadtxt(prefix + "S.txt", delimiter=",")
dG0 = np.loadtxt(prefix + "dG0.txt", delimiter=",")
dG0 = np.reshape(dG0, (dG0.shape[0], 1))
html_writer = HtmlWriter("../res/regression_fast.html")
html_writer.write("<h1>Pseudoisomeric Reactant Contributions</h1>\n")
html_writer.write("<p>The stoichiometric matrix (S):")
html_writer.insert_toggle(start_here=True)
stoichiometric_matrix2html(html_writer, S, cids)
html_writer.div_end()
html_writer.write("</p>")
index2value = {}
S_extended = S # the stoichiometric matrix, extended with elementary basis vector for the fixed compounds
for cid in fixed_cids.keys():
i = cids.index(cid)
e_i = np.zeros((1, len(cids)))
e_i[0, i] = 1.0
S_extended = np.vstack([S_extended, e_i])
nH, dG0_fixed = fixed_cids[cid]
index2value[i] = dG0_fixed
x, _K = LinearRegression.LeastSquaresWithFixedPoints(S, dG0, index2value)
cid2dG0 = {}
for i, cid in enumerate(cids):
cid2dG0[cid] = x[i]
# Calculate the Kernel of the reduced stoichiometric matrix (after removing
# the columns of the fixed compounds).
cids_red = [cid for cid in cids if cid not in fixed_cids]
index_red = [i for i in xrange(len(cids)) if i not in index2value]
S_red = S[:, index_red]
K_red = LinearRegression.Kernel(S_red)
# print "Reduced Stoichiometric Matrix:"
# print matrix2string(S_red, cids_red, kegg)
# print '-'*80
# Find all CIDs that are completely determined and do not depend on any
# free variable. In other words, all zeros columns in K2.
dict_list = []
determined_indices = np.where(np.sum(abs(K_red), 0) < 1e-10)[0] # all zero-columns in reducedK
determined_cids = [cids_red[i] for i in determined_indices]
plot_data = []
for i, cid in enumerate(cids):
d = {
"CID": "C%05d" % cid,
"Compound": kegg.cid2name(cid),
"nH": "%d" % cid2nH[cid],
"dG0 (PRC)": "%.1f" % cid2dG0[cid],
}
if cid in alberty_cid2dG0:
d["dG0 (Alberty)"] = "%.1f" % alberty_cid2dG0[cid]
if cid not in fixed_cids:
plot_data.append((alberty_cid2dG0[cid], cid2dG0[cid], kegg.cid2name(cid)))
else:
d["dG0 (Alberty)"] = ""
if cid in fixed_cids:
d["Depends on"] = "anchored"
elif cid in determined_cids:
d["Depends on"] = "fixed compounds"
else:
d["Depends on"] = "kernel dimensions"
dict_list.append(d)
dict_list.sort(key=lambda (x): (x["Depends on"], x["CID"]))
html_writer.write("<p>Formation energies determined by the linear constraints:")
html_writer.insert_toggle(start_here=True)
html_writer.write('<font size="1">')
html_writer.write_table(
dict_list, headers=["#", "Compound", "CID", "nH", "dG0 (PRC)", "dG0 (Alberty)", "Depends on"]
)
html_writer.write("</font>")
html_writer.div_end()
html_writer.write("</p>")
# Plot a comparison between PRC and Alberty formation energies
fig = plt.figure(figsize=(8, 8), dpi=80)
plt.plot([x[0] for x in plot_data], [x[1] for x in plot_data], "b.", figure=fig)
for x, y, name in plot_data:
plt.text(x, y, name, fontsize=6)
plt.xlabel("Alberty $\Delta_f G^\circ$")
plt.ylabel("PRC $\Delta_f G^\circ$")
html_writer.write("<p>Plot comparing PRC and Alberty results:")
html_writer.insert_toggle(start_here=True)
html_writer.embed_matplotlib_figure(fig)
html_writer.div_end()
html_writer.write("</p>")
K_sparse = SparseKernel(S_red).Solve()
html_writer.write("<p>The sparse null-space of the reduced stoichiometric matrix:")
html_writer.insert_toggle(start_here=True)
stoichiometric_matrix2html(html_writer, K_sparse, cids_red)
html_writer.div_end()
html_writer.write("</p>")
dict_list = []
index2string_html = dict((i, "V<sub>%02d</sub>" % i) for i in xrange(K_sparse.shape[0]))
index2string = dict((i, "V%d" % i) for i in xrange(K_sparse.shape[0]))
for i, cid in enumerate(cids_red):
d = {}
d["KEGG ID"] = '<a href="%s">C%05d</a>' % (kegg.cid2link(cid), cid)
d["KEGG ID plain"] = "C%05d" % cid
d["Compound"] = kegg.cid2name(cid)
d["nH"] = "%d" % cid2nH[cid]
if cid in alberty_cid2dG0:
d["dG0 (Alberty)"] = "%.1f" % alberty_cid2dG0[cid]
else:
d["dG0 (Alberty)"] = ""
d["dG0 (PRC)"] = "%.1f" % cid2dG0[cid]
d["dG0 (PRC) plain"] = "%.1f" % cid2dG0[cid]
indic = np.where(abs(K_sparse[:, i]) > 1e-10, 1, 0).tolist()
indic.reverse()
d["order_key"] = indic
if mlab.rms_flat(K_sparse[:, i]) > 1e-10:
d["dG0 (PRC)"] += " + (" + vector2string(K_sparse[:, i], index2string_html) + ")"
d["dG0 (PRC) plain"] += " + (" + vector2string(K_sparse[:, i], index2string) + ")"
dict_list.append(d)
dict_list.sort(key=lambda (d): (d["order_key"], d["KEGG ID plain"]))
# Export the results to CSV
csv_writer = csv.writer(open("../res/prc_results.csv", "w"))
csv_writer.writerow(["KEGG ID", "Compound", "nH", "dG0 (PRC)", "dG0 (Alberty)"])
for d in dict_list:
csv_writer.writerow([d["KEGG ID plain"], d["Compound"], d["nH"], d["dG0 (PRC) plain"], d["dG0 (Alberty)"]])
html_writer.write("<p>All formation energies as a function of the free variables:")
html_writer.insert_toggle(start_here=True)
html_writer.write('<font size="1">')
html_writer.write_table(dict_list, headers=["#", "KEGG ID", "Compound", "nH", "dG0 (PRC)", "dG0 (Alberty)"])
html_writer.write("</font>")
html_writer.div_end()
html_writer.write("</p>")
fp = open("../res/prc_latex.txt", "w")
fp.write(
latex.table2LaTeX(
dict_list, headers=["#", "KEGG ID plain", "Compound", "nH", "dG0 (PRC) plain", "dG0 (Alberty)"]
)
)
fp.close()
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 main():
html_writer = HtmlWriter("../res/nist/report.html")
estimators = LoadAllEstimators()
nist = Nist()
nist.T_range = (273.15 + 24, 273.15 + 40)
#nist.override_I = 0.25
#nist.override_pMg = 14.0
#nist.override_T = 298.15
html_writer.write('<p>\n')
html_writer.write("Total number of reaction in NIST: %d</br>\n" % len(nist.data))
html_writer.write("Total number of reaction in range %.1fK < T < %.1fK: %d</br>\n" % \
(nist.T_range[0], nist.T_range[1], len(nist.SelectRowsFromNist())))
html_writer.write('</p>\n')
reactions = {}
reactions['KEGG'] = []
for reaction in Kegg.getInstance().AllReactions():
try:
reaction.Balance(balance_water=True, exception_if_unknown=True)
reactions['KEGG'].append(reaction)
except (KeggReactionNotBalancedException, KeggParseException, OpenBabelError):
pass
reactions['FEIST'] = Feist.FromFiles().reactions
reactions['NIST'] = nist.GetUniqueReactionSet()
pairs = []
#pairs += [('hatzi_gc', 'UGC')], ('PGC', 'PRC'), ('alberty', 'PRC')]
for t1, t2 in pairs:
logging.info('Writing the NIST report for %s vs. %s' %
(estimators[t1].name, estimators[t2].name))
html_writer.write('<p><b>%s vs. %s</b> ' %
(estimators[t1].name, estimators[t2].name))
html_writer.insert_toggle(start_here=True)
two_way_comparison(html_writer=html_writer,
thermo1=estimators[t1],
thermo2=estimators[t2],
reaction_list=reactions['FEIST'],
name='%s_vs_%s' % (t1, t2))
html_writer.div_end()
html_writer.write('</p>')
if False:
estimators['alberty'].CompareOverKegg(html_writer,
other=estimators['PRC'],
fig_name='kegg_compare_alberty_vs_nist')
rowdicts = []
rowdict = {'Method': 'Total'}
for db_name, reaction_list in reactions.iteritems():
rowdict[db_name + ' coverage'] = len(reaction_list)
rowdicts.append(rowdict)
for name in ['UGC', 'PGC', 'PRC', 'alberty', 'merged', 'hatzi_gc']:
thermo = estimators[name]
logging.info('Writing the NIST report for %s' % thermo.name)
html_writer.write('<p><b>%s</b> ' % thermo.name)
html_writer.insert_toggle(start_here=True)
num_estimations, rmse = nist.verify_results(html_writer=html_writer,
thermodynamics=thermo,
name=name)
html_writer.div_end()
html_writer.write('N = %d, RMSE = %.1f</p>\n' % (num_estimations, rmse))
logging.info('N = %d, RMSE = %.1f' % (num_estimations, rmse))
rowdict = {'Method':thermo.name,
'RMSE (kJ/mol)':"%.1f (N=%d)" % (rmse, num_estimations)}
for db_name, reaction_list in reactions.iteritems():
n_covered = thermo.CalculateCoverage(reaction_list)
percent = n_covered * 100.0 / len(reaction_list)
rowdict[db_name + " coverage"] = "%.1f%% (%d)" % (percent, n_covered)
logging.info(db_name + " coverage = %.1f%%" % percent)
rowdicts.append(rowdict)
headers = ['Method', 'RMSE (kJ/mol)'] + \
[db_name + ' coverage' for db_name in reactions.keys()]
html_writer.write_table(rowdicts, headers=headers)
def main():
kegg = Kegg.getInstance()
prefix = '../res/prc_'
fixed_cids = {} # a dictionary from CID to pairs of (nH, dG0)
# Alberty formation energies directly measured, linearly independent:
fixed_cids[1] = (2, -237.19) # H2O
fixed_cids[9] = (1, -1096.1) # HPO3(-2)
fixed_cids[14] = (4, -79.31) # NH4(+1)
fixed_cids[59] = (0, -744.53) # SO4(-2)
fixed_cids[288] = (1, -586.77) # HCO3(-1)
# Alberty zeros:
fixed_cids[3] = (26, 0.0) # NAD(ox)
fixed_cids[10] = (32, 0.0) # CoA
fixed_cids[127] = (30, 0.0) # glutathione(ox)
fixed_cids[376] = (28, 0.0) # retinal(ox)
# Directly measured values
fixed_cids[4] = (27, 22.65) # NAD(red) -- relative to NAD(ox)
fixed_cids[212] = (13, -194.5) # adenosine
#fixed_cids[294] = (12, -409.2) # inosine - linearly dependent on other 'anchors'
# Alberty zeros which are not in NIST:
#fixed_cids[524] = ( 0, 0.0) # cytochrome c(ox)
#fixed_cids[16] = (31, 0.0) # FAD(ox)
#fixed_cids[139] = ( 0, 0.0) # ferredoxin(ox)
#fixed_cids[61] = (19, 0.0) # FMN(ox)
#fixed_cids[343] = ( 0, 0.0) # thioredoxin(ox)
#fixed_cids[399] = (90, 0.0) # ubiquinone(ox)
public_db = SqliteDatabase("../data/public_data.sqlite")
alberty = PsuedoisomerTableThermodynamics.FromDatabase(
public_db, 'alberty_pseudoisomers', label=None, name='Alberty')
alberty_cid2dG0 = {}
alberty_cid2nH = {}
for cid in alberty.get_all_cids():
pmap = alberty.cid2PseudoisomerMap(cid)
dG0, _dG0_tag, nH, _z, _nMg = pmap.GetMostAbundantPseudoisomer(
pH=default_pH, I=default_I, pMg=default_pMg, T=default_T)
alberty_cid2nH[cid] = nH
alberty_cid2dG0[cid] = dG0
if not os.path.exists(prefix + 'S.txt'):
db = SqliteDatabase("../res/gibbs.sqlite")
nist_regression = NistRegression(db)
cid2nH = {}
for cid in nist_regression.nist.GetAllCids():
if cid in fixed_cids:
cid2nH[cid] = fixed_cids[cid][0]
elif cid in alberty_cid2nH:
cid2nH[cid] = alberty_cid2nH[cid]
else:
tmp = nist_regression.dissociation.GetMostAbundantPseudoisomer(
cid,
pH=default_pH,
I=default_I,
pMg=default_pMg,
T=default_T)
if tmp is not None:
cid2nH[cid] = tmp[0]
else:
logging.warning(
'The most abundant pseudoisomer of %s (C%05d) '
'cannot be resolved. Using nH = 0.' %
(kegg.cid2name(cid), cid))
cid2nH[cid] = 0
#nist_regression.std_diff_threshold = 2.0 # the threshold over which to print an analysis of a reaction
#nist_regression.nist.T_range = None#(273.15 + 24, 273.15 + 40)
S, dG0, cids = nist_regression.ReverseTransform(cid2nH=cid2nH)
# export the raw data matrices to text files
C = np.array([[cid, cid2nH.get(cid, 0)] for cid in cids])
np.savetxt(prefix + 'CID.txt', C, fmt='%d', delimiter=',')
np.savetxt(prefix + 'S.txt', S, fmt='%g', delimiter=',')
np.savetxt(prefix + 'dG0.txt', dG0, fmt='%.2f', delimiter=',')
else:
C = np.loadtxt(prefix + 'CID.txt', delimiter=',')
cids = [int(cid) for cid in C[:, 0]]
cid2nH = {}
for i, cid in enumerate(cids):
cid2nH[cid] = int(C[i, 1])
S = np.loadtxt(prefix + 'S.txt', delimiter=',')
dG0 = np.loadtxt(prefix + 'dG0.txt', delimiter=',')
dG0 = np.reshape(dG0, (dG0.shape[0], 1))
html_writer = HtmlWriter('../res/regression_fast.html')
html_writer.write("<h1>Pseudoisomeric Reactant Contributions</h1>\n")
html_writer.write("<p>The stoichiometric matrix (S):")
html_writer.insert_toggle(start_here=True)
stoichiometric_matrix2html(html_writer, S, cids)
html_writer.div_end()
html_writer.write('</p>')
index2value = {}
S_extended = S # the stoichiometric matrix, extended with elementary basis vector for the fixed compounds
for cid in fixed_cids.keys():
i = cids.index(cid)
e_i = np.zeros((1, len(cids)))
e_i[0, i] = 1.0
S_extended = np.vstack([S_extended, e_i])
nH, dG0_fixed = fixed_cids[cid]
index2value[i] = dG0_fixed
x, _K = LinearRegression.LeastSquaresWithFixedPoints(S, dG0, index2value)
cid2dG0 = {}
for i, cid in enumerate(cids):
cid2dG0[cid] = x[i]
# Calculate the Kernel of the reduced stoichiometric matrix (after removing
# the columns of the fixed compounds).
cids_red = [cid for cid in cids if cid not in fixed_cids]
index_red = [i for i in xrange(len(cids)) if i not in index2value]
S_red = S[:, index_red]
K_red = LinearRegression.Kernel(S_red)
#print "Reduced Stoichiometric Matrix:"
#print matrix2string(S_red, cids_red, kegg)
#print '-'*80
# Find all CIDs that are completely determined and do not depend on any
# free variable. In other words, all zeros columns in K2.
dict_list = []
determined_indices = np.where(
np.sum(abs(K_red), 0) < 1e-10)[0] # all zero-columns in reducedK
determined_cids = [cids_red[i] for i in determined_indices]
plot_data = []
for i, cid in enumerate(cids):
d = {
'CID': 'C%05d' % cid,
'Compound': kegg.cid2name(cid),
'nH': '%d' % cid2nH[cid],
'dG0 (PRC)': '%.1f' % cid2dG0[cid]
}
if cid in alberty_cid2dG0:
d['dG0 (Alberty)'] = '%.1f' % alberty_cid2dG0[cid]
if cid not in fixed_cids:
plot_data.append(
(alberty_cid2dG0[cid], cid2dG0[cid], kegg.cid2name(cid)))
else:
d['dG0 (Alberty)'] = ''
if cid in fixed_cids:
d['Depends on'] = 'anchored'
elif cid in determined_cids:
d['Depends on'] = 'fixed compounds'
else:
d['Depends on'] = 'kernel dimensions'
dict_list.append(d)
dict_list.sort(key=lambda (x): (x['Depends on'], x['CID']))
html_writer.write(
"<p>Formation energies determined by the linear constraints:")
html_writer.insert_toggle(start_here=True)
html_writer.write('<font size="1">')
html_writer.write_table(dict_list,
headers=[
'#', 'Compound', 'CID', 'nH', 'dG0 (PRC)',
'dG0 (Alberty)', 'Depends on'
])
html_writer.write('</font>')
html_writer.div_end()
html_writer.write('</p>')
# Plot a comparison between PRC and Alberty formation energies
fig = plt.figure(figsize=(8, 8), dpi=80)
plt.plot([x[0] for x in plot_data], [x[1] for x in plot_data],
'b.',
figure=fig)
for x, y, name in plot_data:
plt.text(x, y, name, fontsize=6)
plt.xlabel('Alberty $\Delta_f G^\circ$')
plt.ylabel('PRC $\Delta_f G^\circ$')
html_writer.write("<p>Plot comparing PRC and Alberty results:")
html_writer.insert_toggle(start_here=True)
html_writer.embed_matplotlib_figure(fig)
html_writer.div_end()
html_writer.write("</p>")
K_sparse = SparseKernel(S_red).Solve()
html_writer.write(
"<p>The sparse null-space of the reduced stoichiometric matrix:")
html_writer.insert_toggle(start_here=True)
stoichiometric_matrix2html(html_writer, K_sparse, cids_red)
html_writer.div_end()
html_writer.write("</p>")
dict_list = []
index2string_html = dict(
(i, "V<sub>%02d</sub>" % i) for i in xrange(K_sparse.shape[0]))
index2string = dict((i, "V%d" % i) for i in xrange(K_sparse.shape[0]))
for i, cid in enumerate(cids_red):
d = {}
d['KEGG ID'] = '<a href="%s">C%05d</a>' % (kegg.cid2link(cid), cid)
d['KEGG ID plain'] = 'C%05d' % cid
d['Compound'] = kegg.cid2name(cid)
d['nH'] = '%d' % cid2nH[cid]
if cid in alberty_cid2dG0:
d['dG0 (Alberty)'] = '%.1f' % alberty_cid2dG0[cid]
else:
d['dG0 (Alberty)'] = ''
d['dG0 (PRC)'] = '%.1f' % cid2dG0[cid]
d['dG0 (PRC) plain'] = '%.1f' % cid2dG0[cid]
indic = np.where(abs(K_sparse[:, i]) > 1e-10, 1, 0).tolist()
indic.reverse()
d['order_key'] = indic
if mlab.rms_flat(K_sparse[:, i]) > 1e-10:
d['dG0 (PRC)'] += " + (" + vector2string(K_sparse[:, i],
index2string_html) + ")"
d['dG0 (PRC) plain'] += " + (" + vector2string(
K_sparse[:, i], index2string) + ")"
dict_list.append(d)
dict_list.sort(key=lambda (d): (d['order_key'], d['KEGG ID plain']))
# Export the results to CSV
csv_writer = csv.writer(open('../res/prc_results.csv', 'w'))
csv_writer.writerow(
['KEGG ID', 'Compound', 'nH', 'dG0 (PRC)', 'dG0 (Alberty)'])
for d in dict_list:
csv_writer.writerow([
d['KEGG ID plain'], d['Compound'], d['nH'], d['dG0 (PRC) plain'],
d['dG0 (Alberty)']
])
html_writer.write(
"<p>All formation energies as a function of the free variables:")
html_writer.insert_toggle(start_here=True)
html_writer.write('<font size="1">')
html_writer.write_table(dict_list,
headers=[
'#', 'KEGG ID', 'Compound', 'nH', 'dG0 (PRC)',
'dG0 (Alberty)'
])
html_writer.write('</font>')
html_writer.div_end()
html_writer.write('</p>')
fp = open('../res/prc_latex.txt', 'w')
fp.write(
latex.table2LaTeX(dict_list,
headers=[
'#', 'KEGG ID plain', 'Compound', 'nH',
'dG0 (PRC) plain', 'dG0 (Alberty)'
]))
fp.close()
