def AnalyzeConcentrationGradient(prefix,
thermo,
csv_output_fname,
cid=13): # default compound is PPi
compound_name = thermo.kegg.cid2name(cid)
kegg_file = ParsedKeggFile.FromKeggFile('../data/thermodynamics/%s.txt' %
prefix)
html_writer = HtmlWriter('../res/%s.html' % prefix)
null_html_writer = NullHtmlWriter()
if csv_output_fname:
csv_output = csv.writer(open(csv_output_fname, 'w'))
csv_output.writerow(['pH', 'I', 'T', '[C%05d]' % cid] +
kegg_file.entries())
else:
csv_output = None
pH_vec = np.array(
[7]) # this needs to be fixed so that the txt file will set the pH
conc_vec = 10**(-np.arange(2, 6.0001, 0.25)
) # logarithmic scale between 10mM and 1nM
override_bounds = {}
fig = plt.figure(figsize=(6, 6), dpi=90)
legend = []
for pH in pH_vec.flat:
obd_vec = []
for conc in conc_vec.flat:
override_bounds[cid] = (conc, conc)
logging.info("pH = %g, [%s] = %.1e M" % (pH, compound_name, conc))
data, labels = pareto(kegg_file,
null_html_writer,
thermo,
pH=pH,
section_prefix="",
balance_water=True,
override_bounds=override_bounds)
obd_vec.append(data[:, 1])
csv_output.writerow([pH, thermo.I, thermo.T, conc] +
list(data[:, 1].flat))
obd_mat = np.matrix(
obd_vec) # rows are pathways and columns are concentrations
plt.plot(conc_vec, obd_mat, '.-', figure=fig)
legend += ['%s, pH = %g' % (l, pH) for l in labels]
plt.title("ODB vs. [%s] (I = %gM, T = %gK)" %
(compound_name, thermo.I, thermo.T),
figure=fig)
plt.xscale('log')
plt.xlabel('Concentration of %s [M]' % thermo.kegg.cid2name(cid),
figure=fig)
plt.ylabel('Optimized Distributed Bottleneck [kJ/mol]', figure=fig)
plt.legend(legend)
html_writer.write('<h2 id="figure_%s">Summary figure</h1>\n' % prefix)
html_writer.embed_matplotlib_figure(fig, name=prefix)
html_writer.close()
def main():
html_writer = HtmlWriter("../res/formation_resolve.html")
estimators = LoadAllEstimators()
for name in ['alberty']:
thermo = estimators[name]
nist = Nist()
nist.verify_formation(html_writer=html_writer,
thermodynamics=thermo,
name=name)
html_writer.close()
def AnalyzePareto(pathway_file, output_prefix, thermo, pH=None):
pathway_list = KeggFile2PathwayList(pathway_file)
pathway_names = [entry for (entry, _) in pathway_list]
html_writer = HtmlWriter('%s.html' % output_prefix)
xls_workbook = Workbook()
logging.info("running OBD analysis for all pathways")
data = GetAllOBDs(pathway_list,
html_writer,
thermo,
pH=pH,
section_prefix="pareto",
balance_water=True,
override_bounds={})
for d in data:
sheet = xls_workbook.add_sheet(d['entry'])
sheet.write(0, 0, "reaction")
sheet.write(0, 1, "formula")
sheet.write(0, 2, "flux")
sheet.write(0, 3, "delta_r G'")
sheet.write(0, 4, "shadow price")
for r, rid in enumerate(d['rids']):
sheet.write(r + 1, 0, rid)
sheet.write(r + 1, 1, d['formulas'][r])
sheet.write(r + 1, 2, d['fluxes'][0, r])
sheet.write(r + 1, 3, d['dG_r_prime'][0, r])
sheet.write(r + 1, 4, d['reaction prices'][r, 0])
xls_workbook.save('%s.xls' % output_prefix)
obds = []
minus_avg_tg = []
for i, d in enumerate(data):
obds.append(d['OBD'])
if d['sum of fluxes']:
minus_avg_tg.append(-d['max total dG'] / d['sum of fluxes'])
else:
minus_avg_tg.append(0)
fig = plt.figure(figsize=(6, 6), dpi=90)
plt.plot(minus_avg_tg, obds, 'o', figure=fig)
plt.plot([0, max(minus_avg_tg)], [0, max(minus_avg_tg)], '--g')
for i, name in enumerate(pathway_names):
plt.text(minus_avg_tg[i], obds[i], name)
plt.title('OBD vs. Average $\Delta_r G$')
plt.ylim(ymin=0)
plt.xlim(xmin=0)
plt.xlabel(r'- Average $\Delta_r G$ [kJ/mol]')
plt.ylabel(r'Optimized Distributed Bottleneck [kJ/mol]')
html_writer.write('<h2>Pareto figure</h1>\n')
html_writer.embed_matplotlib_figure(fig)
html_writer.close()
def AnalyzePHGradient(pathway_file, output_prefix, thermo, conc_range):
pathway_list = KeggFile2PathwayList(pathway_file)
pathway_names = [entry for (entry, _) in pathway_list]
html_writer = HtmlWriter('%s.html' % output_prefix)
# run once just to make sure that the pathways are all working:
logging.info("testing all pathways with default pH")
data = GetAllOBDs(pathway_list, html_writer, thermo,
pH=None, section_prefix="test", balance_water=True,
override_bounds={})
csv_output = csv.writer(open('%s.csv' % output_prefix, 'w'))
csv_output.writerow(['pH'] + pathway_names)
util._mkdir(output_prefix)
shadow_csvs = {}
for d in data:
path = '%s/%s.csv' % (output_prefix, d['entry'])
shadow_csvs[d['entry']] = csv.writer(open(path, 'w'))
shadow_csvs[d['entry']].writerow(['pH'] + d['rids'])
pH_vec = ParseConcentrationRange(conc_range)
obd_mat = []
for pH in pH_vec.flat:
logging.info("pH = %.1f" % (pH))
data = GetAllOBDs(pathway_list, html_writer=None, thermo=thermo,
pH=pH, section_prefix="", balance_water=True,
override_bounds={})
obds = [d['OBD'] for d in data]
obd_mat.append(obds)
csv_output.writerow([data[0]['pH']] + obds)
for d in data:
if type(d['reaction prices']) != types.FloatType:
prices = list(d['reaction prices'].flat)
shadow_csvs[d['entry']].writerow([pH] + prices)
obd_mat = np.matrix(obd_mat) # rows are pathways and columns are concentrations
fig = plt.figure(figsize=(6, 6), dpi=90)
colormap = color.ColorMap(pathway_names)
for i, name in enumerate(pathway_names):
plt.plot(pH_vec, obd_mat[:, i], '-', color=colormap[name],
figure=fig)
plt.title("OBD vs. pH", figure=fig)
plt.ylim(0, np.max(obd_mat.flat))
plt.xlabel('pH', figure=fig)
plt.ylabel('Optimized Distributed Bottleneck [kJ/mol]', figure=fig)
plt.legend(pathway_names)
html_writer.write('<h2>Summary figure</h1>\n')
html_writer.embed_matplotlib_figure(fig)
html_writer.close()
def compare_charges():
#db_public = SqliteDatabase('../data/public_data.sqlite')
db_gibbs = SqliteDatabase('../res/gibbs.sqlite')
print "Writing Compare Charges report to ../res/groups_report.html"
html_writer = HtmlWriter("../res/groups_report.html")
kegg = Kegg.getInstance()
#pH, I, pMg, T = default_pH, default_I, default_pMg, default_T
pH, I, pMg, T = default_pH, 0, 14, default_T
cid2error = {}
for row_dict in db_gibbs.DictReader("gc_errors"):
cid = int(row_dict['cid'])
cid2error[cid] = row_dict['error']
estimators = {}
estimators['hatzi'] = Hatzi(use_pKa=False)
estimators['milo'] = PsuedoisomerTableThermodynamics.FromDatabase(
db_gibbs, 'gc_pseudoisomers', name='Milo Group Contribution')
all_cids = set(lsum([e.get_all_cids() for e in estimators.values()]))
dict_list = []
for cid in all_cids:
try:
name = kegg.cid2name(cid)
link = kegg.cid2compound(cid).get_link()
except KeyError:
name = "unknown"
link = ""
row_dict = {
'cid': '<a href="%s">C%05d</a>' % (link, cid),
'name': name,
'error': cid2error.get(cid, None)
}
for key, est in estimators.iteritems():
try:
pmap = est.cid2PseudoisomerMap(cid)
dG0, dG0_tag, nH, z, nMg = pmap.GetMostAbundantPseudoisomer(
pH, I, pMg, T)
except MissingCompoundFormationEnergy:
dG0, dG0_tag, nH, z, nMg = "", "", "", "", ""
row_dict['nH_' + key] = nH
row_dict['charge_' + key] = z
row_dict['nMg_' + key] = nMg
row_dict['dG0_' + key] = dG0
row_dict['dG0_tag_' + key] = dG0_tag
dict_list.append(row_dict)
html_writer.write_table(
dict_list,
headers=['cid', 'name', 'charge_hatzi', 'charge_milo', 'error'])
html_writer.close()
def AnalyzePareto(pathway_file, output_prefix, thermo, pH=None):
pathway_list = KeggFile2PathwayList(pathway_file)
pathway_names = [entry for (entry, _) in pathway_list]
html_writer = HtmlWriter('%s.html' % output_prefix)
xls_workbook = Workbook()
logging.info("running OBD analysis for all pathways")
data = GetAllOBDs(pathway_list, html_writer, thermo,
pH=pH, section_prefix="pareto", balance_water=True,
override_bounds={})
for d in data:
sheet = xls_workbook.add_sheet(d['entry'])
sheet.write(0, 0, "reaction")
sheet.write(0, 1, "formula")
sheet.write(0, 2, "flux")
sheet.write(0, 3, "delta_r G'")
sheet.write(0, 4, "shadow price")
for r, rid in enumerate(d['rids']):
sheet.write(r+1, 0, rid)
sheet.write(r+1, 1, d['formulas'][r])
sheet.write(r+1, 2, d['fluxes'][0, r])
sheet.write(r+1, 3, d['dG_r_prime'][0, r])
sheet.write(r+1, 4, d['reaction prices'][r, 0])
xls_workbook.save('%s.xls' % output_prefix)
obds = []
minus_avg_tg = []
for i, d in enumerate(data):
obds.append(d['OBD'])
if d['sum of fluxes']:
minus_avg_tg.append(-d['max total dG']/d['sum of fluxes'])
else:
minus_avg_tg.append(0)
fig = plt.figure(figsize=(6, 6), dpi=90)
plt.plot(minus_avg_tg, obds, 'o', figure=fig)
plt.plot([0, max(minus_avg_tg)], [0, max(minus_avg_tg)], '--g')
for i, name in enumerate(pathway_names):
plt.text(minus_avg_tg[i], obds[i], name)
plt.title('OBD vs. Average $\Delta_r G$')
plt.ylim(ymin=0)
plt.xlim(xmin=0)
plt.xlabel(r'- Average $\Delta_r G$ [kJ/mol]')
plt.ylabel(r'Optimized Distributed Bottleneck [kJ/mol]')
html_writer.write('<h2>Pareto figure</h1>\n')
html_writer.embed_matplotlib_figure(fig)
html_writer.close()
def compare_charges():
#db_public = SqliteDatabase('../data/public_data.sqlite')
db_gibbs = SqliteDatabase('../res/gibbs.sqlite')
print "Writing Compare Charges report to ../res/groups_report.html"
html_writer = HtmlWriter("../res/groups_report.html")
kegg = Kegg.getInstance()
#pH, I, pMg, T = default_pH, default_I, default_pMg, default_T
pH, I, pMg, T = default_pH, 0, 14, default_T
cid2error = {}
for row_dict in db_gibbs.DictReader("gc_errors"):
cid = int(row_dict['cid'])
cid2error[cid] = row_dict['error']
estimators = {}
estimators['hatzi'] = Hatzi(use_pKa=False)
estimators['milo'] = PsuedoisomerTableThermodynamics.FromDatabase(
db_gibbs, 'gc_pseudoisomers', name='Milo Group Contribution')
all_cids = set(lsum([e.get_all_cids() for e in estimators.values()]))
dict_list = []
for cid in all_cids:
try:
name = kegg.cid2name(cid)
link = kegg.cid2compound(cid).get_link()
except KeyError:
name = "unknown"
link = ""
row_dict = {'cid':'<a href="%s">C%05d</a>' % (link, cid),
'name':name, 'error':cid2error.get(cid, None)}
for key, est in estimators.iteritems():
try:
pmap = est.cid2PseudoisomerMap(cid)
dG0, dG0_tag, nH, z, nMg = pmap.GetMostAbundantPseudoisomer(pH, I, pMg, T)
except MissingCompoundFormationEnergy:
dG0, dG0_tag, nH, z, nMg = "", "", "", "", ""
row_dict['nH_' + key] = nH
row_dict['charge_' + key] = z
row_dict['nMg_' + key] = nMg
row_dict['dG0_' + key] = dG0
row_dict['dG0_tag_' + key] = dG0_tag
dict_list.append(row_dict)
html_writer.write_table(dict_list, headers=['cid', 'name', 'charge_hatzi', 'charge_milo', 'error'])
html_writer.close()
def AnalyzeConcentrationGradient(pathway_file, output_prefix, thermo, conc_range, cids=[], pH=None):
compound_names = ','.join([thermo.kegg.cid2name(cid) for cid in cids])
pathway_list = KeggFile2PathwayList(pathway_file)
pathway_names = [entry for (entry, _) in pathway_list]
html_writer = HtmlWriter('%s.html' % output_prefix)
# run once just to make sure that the pathways are all working:
logging.info("testing all pathways with default concentrations")
data = GetAllOBDs(pathway_list, html_writer, thermo,
pH=pH, section_prefix="test", balance_water=True,
override_bounds={})
csv_output = csv.writer(open('%s.csv' % output_prefix, 'w'))
csv_output.writerow(['pH', '[' + compound_names + ']'] + pathway_names)
conc_vec = 10**(-ParseConcentrationRange(conc_range)) # logarithmic scale between 10mM and 1nM
override_bounds = {}
obd_mat = []
for conc in conc_vec.flat:
for cid in cids:
override_bounds[cid] = (conc, conc)
logging.info("[%s] = %.1e M" % (compound_names, conc))
data = GetAllOBDs(pathway_list, html_writer=None, thermo=thermo,
pH=pH, section_prefix="", balance_water=True,
override_bounds=override_bounds)
obds = [d['OBD'] for d in data]
obd_mat.append(obds)
csv_output.writerow([data[0]['pH'], conc] + obds)
obd_mat = np.matrix(obd_mat) # rows are pathways and columns are concentrations
fig = plt.figure(figsize=(6, 6), dpi=90)
colormap = color.ColorMap(pathway_names)
for i, name in enumerate(pathway_names):
plt.plot(conc_vec, obd_mat[:, i], '-', color=colormap[name],
figure=fig)
plt.title("OBD vs. [%s]" % (compound_names), figure=fig)
plt.xscale('log')
plt.ylim(ymin=0)
plt.xlabel('[%s] (in M)' % compound_names, figure=fig)
plt.ylabel('Optimized Distributed Bottleneck [kJ/mol]', figure=fig)
plt.legend(pathway_names)
html_writer.write('<h2>Summary figure</h1>\n')
html_writer.embed_matplotlib_figure(fig)
html_writer.close()
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():
estimators = LoadAllEstimators()
parser = MakeArgParser(estimators)
args = parser.parse_args()
thermo = estimators[args.thermodynamics_source]
kegg_file = ParsedKeggFile.FromKeggFile(args.config_fname)
entries = kegg_file.entries()
if len(entries) == 0:
raise ValueError('No entries in configuration file')
entry = 'CONFIGURATION'
if entry not in entries:
logging.warning(
'Configuration file does not contain the entry "CONFIGURATION". '
'Using the first entry by default: %s' % entries[0])
entry = entries[0]
p_data = PathwayData.FromFieldMap(kegg_file[entry])
thermo.SetConditions(pH=p_data.pH, I=p_data.I, T=p_data.T, pMg=p_data.pMg)
thermo.c_range = p_data.c_range
bounds = p_data.GetBounds()
html_writer = HtmlWriter(args.output_prefix + ".html")
rowdicts = []
headers = ['Module', 'Name', 'OBD [kJ/mol]', 'Length']
kegg = Kegg.getInstance()
for mid in kegg.get_all_mids():
html_writer.write('<h2 id=M%05d>M%05d: %s</h2>' %
(mid, mid, kegg.get_module_name(mid)))
try:
d = AnalyzeKeggModule(thermo, mid, bounds, html_writer)
except KeyError:
continue
d['Module'] = '<a href="#M%05d">M%05d</a>' % (mid, mid)
d['Name'] = kegg.get_module_name(mid)
rowdicts.append(d)
rowdicts.sort(key=lambda x: x['OBD [kJ/mol]'])
html_writer.write_table(rowdicts, headers, decimal=1)
html_writer.close()
def main():
estimators = LoadAllEstimators()
parser = MakeArgParser(estimators)
args = parser.parse_args()
thermo = estimators[args.thermodynamics_source]
kegg_file = ParsedKeggFile.FromKeggFile(args.config_fname)
entries = kegg_file.entries()
if len(entries) == 0:
raise ValueError('No entries in configuration file')
entry = 'CONFIGURATION'
if entry not in entries:
logging.warning('Configuration file does not contain the entry "CONFIGURATION". '
'Using the first entry by default: %s' % entries[0])
entry = entries[0]
p_data = PathwayData.FromFieldMap(kegg_file[entry])
thermo.SetConditions(pH=p_data.pH, I=p_data.I, T=p_data.T, pMg=p_data.pMg)
thermo.c_range = p_data.c_range
bounds = p_data.GetBounds()
html_writer = HtmlWriter(args.output_prefix + ".html")
rowdicts = []
headers = ['Module', 'Name', 'OBD [kJ/mol]', 'Length']
kegg = Kegg.getInstance()
for mid in kegg.get_all_mids():
html_writer.write('<h2 id=M%05d>M%05d: %s</h2>' %
(mid, mid, kegg.get_module_name(mid)))
try:
d = AnalyzeKeggModule(thermo, mid, bounds, html_writer)
except KeyError:
continue
d['Module'] = '<a href="#M%05d">M%05d</a>' % (mid, mid)
d['Name'] = kegg.get_module_name(mid)
rowdicts.append(d)
rowdicts.sort(key=lambda x:x['OBD [kJ/mol]'])
html_writer.write_table(rowdicts, headers, decimal=1)
html_writer.close()
def AnalyzeConcentrationGradient(prefix, thermo, csv_output_fname, cid=13): # default compound is PPi
compound_name = thermo.kegg.cid2name(cid)
kegg_file = ParsedKeggFile.FromKeggFile('../data/thermodynamics/%s.txt' % prefix)
html_writer = HtmlWriter('../res/%s.html' % prefix)
null_html_writer = NullHtmlWriter()
if csv_output_fname:
csv_output = csv.writer(open(csv_output_fname, 'w'))
csv_output.writerow(['pH', 'I', 'T', '[C%05d]' % cid] + kegg_file.entries())
else:
csv_output = None
pH_vec = np.array([7]) # this needs to be fixed so that the txt file will set the pH
conc_vec = 10**(-np.arange(2, 6.0001, 0.25)) # logarithmic scale between 10mM and 1nM
override_bounds = {}
fig = plt.figure(figsize=(6, 6), dpi=90)
legend = []
for pH in pH_vec.flat:
obd_vec = []
for conc in conc_vec.flat:
override_bounds[cid] = (conc, conc)
logging.info("pH = %g, [%s] = %.1e M" % (pH, compound_name, conc))
data, labels = pareto(kegg_file, null_html_writer, thermo,
pH=pH, section_prefix="", balance_water=True,
override_bounds=override_bounds)
obd_vec.append(data[:, 1])
csv_output.writerow([pH, thermo.I, thermo.T, conc] + list(data[:, 1].flat))
obd_mat = np.matrix(obd_vec) # rows are pathways and columns are concentrations
plt.plot(conc_vec, obd_mat, '.-', figure=fig)
legend += ['%s, pH = %g' % (l, pH) for l in labels]
plt.title("ODB vs. [%s] (I = %gM, T = %gK)" % (compound_name, thermo.I, thermo.T), figure=fig)
plt.xscale('log')
plt.xlabel('Concentration of %s [M]' % thermo.kegg.cid2name(cid), figure=fig)
plt.ylabel('Optimized Distributed Bottleneck [kJ/mol]', figure=fig)
plt.legend(legend)
html_writer.write('<h2 id="figure_%s">Summary figure</h1>\n' % prefix)
html_writer.embed_matplotlib_figure(fig, name=prefix)
html_writer.close()
def main():
db = database.SqliteDatabase('../res/gibbs.sqlite')
html_writer = HtmlWriter("../res/nist/report.html")
gc = GroupContribution(db)
gc.override_gc_with_measurements = True
gc.init()
grad = GradientAscent(gc)
nist = Nist(db, html_writer, gc.kegg())
nist.FromDatabase()
alberty = Alberty()
hatzi = Hatzi()
if True:
grad.load_nist_data(nist, alberty, skip_missing_reactions=False, T_range=(298, 314))
grad.verify_results("Alberty", alberty, html_writer)
#grad.write_pseudoisomers("../res/nist/nist_dG0_f.csv")
#html_writer.write("<h2>Using Group Contribution (Hatzimanikatis' implementation)</h2>")
#html_writer.write("<h3>Correlation with the reduced NIST database (containing only compounds that appear in Alberty's list)</h3>")
#logging.info("calculate the correlation between Hatzimanikatis' predictions and the reduced NIST database")
#grad.verify_results("Hatzimanikatis_Reduced", hatzi, html_writer)
#grad.load_nist_data(nist, hatzi, skip_missing_reactions=True, T_range=(298, 314))
grad.verify_results("Hatzimanikatis", hatzi, html_writer)
#grad.load_nist_data(nist, gc, skip_missing_reactions=True, T_range=(298, 314))
grad.verify_results("Milo", gc, html_writer)
elif False:
# Run the gradient ascent algorithm, where the starting point is the same file used for training the GC algorithm
grad.load_dG0_data("../data/thermodynamics/dG0.csv")
# load the data for the anchors (i.e. compounds whose dG0 should not be changed - usually their value will be 0).
grad.anchors = grad.load_dG0_data("../data/thermodynamics/nist_anchors.csv")
grad.load_nist_data(nist, grad, skip_missing_reactions=True)
print "Training %d compounds using %d reactions: " % (len(grad.cid2pmap_dict.keys()), len(grad.data))
grad.hill_climb(max_i=20000)
grad.save_energies(grad.gc.comm, "gradient_cid2prm")
grad.verify_results("gradient1")
elif False:
# Run the gradient ascent algorithm, where the starting point is Alberty's table from (Mathematica 2006)
grad.load_nist_data(nist, alberty, skip_missing_reactions=True)
print "Training %d compounds using %d reactions: " % (len(grad.cid2pmap_dict.keys()), len(grad.data))
grad.cid2pmap_dict = alberty.cid2pmap_dict
grad.hill_climb(max_i=20000)
grad.save_energies(grad.gc.comm, "gradient_cid2prm")
grad.verify_results("gradient2")
elif False:
# Run the gradient ascent algorithm, where the starting point is Alberty's table from (Mathematica 2006)
# Use DETERMINISTIC gradient ascent
grad.load_nist_data(nist, alberty, skip_missing_reactions=True, T_range=(24 + 273.15, 40 + 273.15))
print "Training %d compounds using %d reactions: " % (len(grad.cid2pmap_dict.keys()), len(grad.data))
grad.cid2pmap_dict = alberty.cid2pmap_dict
grad.deterministic_hill_climb(max_i=200)
grad.save_energies(grad.gc.comm, "gradient_cid2prm")
grad.verify_results("gradient_deterministic")
elif False:
# Run the gradient ascent algorithm, where the starting point arbitrary (predict all of the NIST compounds)
grad = GradientAscent(gc)
grad.load_nist_data(nist, skip_missing_reactions=False)
print "Training %d compounds using %d reactions: " % (len(grad.cid2pmap_dict.keys()), len(grad.data))
grad.hill_climb(max_i=20000)
grad.save_energies(grad.gc.comm, "gradient_cid2prm")
grad.verify_results("gradient3")
elif False: # Use Alberty's table from (Mathematica 2006) to calculate the dG0 of all possible reactions in KEGG
grad = GradientAscent(gc)
grad.cid2pmap_dict = alberty.cid2pmap_dict
(pH, I, T) = (7, 0, 300)
counter = 0
for rid in grad.kegg.get_all_rids():
sparse_reaction = grad.kegg.rid2sparse_reaction(rid)
try:
dG0 = grad.reaction_to_dG0(sparse_reaction, pH, I, T)
print "R%05d: dG0_r = %.2f [kJ/mol]" % (rid, dG0)
counter += 1
except MissingCompoundFormationEnergy as e:
#print "R%05d: missing formation energy of C%05d" % (rid, e.cid)
pass
print "Managed to calculate the dG0 of %d reactions" % counter
elif False:
util._mkdir("../res/nist/fig")
csv_writer = csv.writer(open("../res/nist/pseudoisomers.csv", "w"))
cid_set = set()
for row in nist.data:
sparce_reaction = row['sparse']
cid_set.update(sparce_reaction.keys())
html_writer.write("<table border=1>\n")
for cid in sorted(list(cid_set)):
html_writer.write(" <tr><td>C%05d</td><td>%s</td><td>" % (cid, grad.kegg.cid2name(cid)))
try:
mol = grad.kegg.cid2mol(cid)
img_fname = '../res/nist/fig/C%05d.png' % cid
html_writer.embed_img(img_fname, "C%05d" % cid)
mol.draw(show=False, filename=img_fname)
except AssertionError as e:
html_writer.write("WARNING: cannot draw C%05d - %s" % (cid, str(e)))
except KeggParseException as e:
html_writer.write("WARNING: cannot draw C%05d - %s" % (cid, str(e)))
html_writer.write("</td><td>")
if (cid in alberty.cid2pmap_dict):
for (nH, z) in alberty.cid2pmap_dict[cid].keys():
html_writer.write("(nH=%d, z=%d)<br>" % (nH, z))
csv_writer.writerow((cid, nH, z))
else:
nH = grad.kegg.cid2num_hydrogens(cid)
z = grad.kegg.cid2charge(cid)
html_writer.write("unknown pseudoisomers<br>")
html_writer.write("(nH=%d, z=%d)" % (nH, z))
csv_writer.writerow((cid, nH, z))
html_writer.write("</td></tr>\n")
html_writer.write("</table>\n")
html_writer.close()
def main():
db = database.SqliteDatabase('../res/gibbs.sqlite')
html_writer = HtmlWriter("../res/nist/report.html")
gc = GroupContribution(db)
gc.override_gc_with_measurements = True
gc.init()
grad = GradientAscent(gc)
nist = Nist(db, html_writer, gc.kegg())
nist.FromDatabase()
alberty = Alberty()
hatzi = Hatzi()
if True:
grad.load_nist_data(nist,
alberty,
skip_missing_reactions=False,
T_range=(298, 314))
grad.verify_results("Alberty", alberty, html_writer)
#grad.write_pseudoisomers("../res/nist/nist_dG0_f.csv")
#html_writer.write("<h2>Using Group Contribution (Hatzimanikatis' implementation)</h2>")
#html_writer.write("<h3>Correlation with the reduced NIST database (containing only compounds that appear in Alberty's list)</h3>")
#logging.info("calculate the correlation between Hatzimanikatis' predictions and the reduced NIST database")
#grad.verify_results("Hatzimanikatis_Reduced", hatzi, html_writer)
#grad.load_nist_data(nist, hatzi, skip_missing_reactions=True, T_range=(298, 314))
grad.verify_results("Hatzimanikatis", hatzi, html_writer)
#grad.load_nist_data(nist, gc, skip_missing_reactions=True, T_range=(298, 314))
grad.verify_results("Milo", gc, html_writer)
elif False:
# Run the gradient ascent algorithm, where the starting point is the same file used for training the GC algorithm
grad.load_dG0_data("../data/thermodynamics/dG0.csv")
# load the data for the anchors (i.e. compounds whose dG0 should not be changed - usually their value will be 0).
grad.anchors = grad.load_dG0_data(
"../data/thermodynamics/nist_anchors.csv")
grad.load_nist_data(nist, grad, skip_missing_reactions=True)
print "Training %d compounds using %d reactions: " % (len(
grad.cid2pmap_dict.keys()), len(grad.data))
grad.hill_climb(max_i=20000)
grad.save_energies(grad.gc.comm, "gradient_cid2prm")
grad.verify_results("gradient1")
elif False:
# Run the gradient ascent algorithm, where the starting point is Alberty's table from (Mathematica 2006)
grad.load_nist_data(nist, alberty, skip_missing_reactions=True)
print "Training %d compounds using %d reactions: " % (len(
grad.cid2pmap_dict.keys()), len(grad.data))
grad.cid2pmap_dict = alberty.cid2pmap_dict
grad.hill_climb(max_i=20000)
grad.save_energies(grad.gc.comm, "gradient_cid2prm")
grad.verify_results("gradient2")
elif False:
# Run the gradient ascent algorithm, where the starting point is Alberty's table from (Mathematica 2006)
# Use DETERMINISTIC gradient ascent
grad.load_nist_data(nist,
alberty,
skip_missing_reactions=True,
T_range=(24 + 273.15, 40 + 273.15))
print "Training %d compounds using %d reactions: " % (len(
grad.cid2pmap_dict.keys()), len(grad.data))
grad.cid2pmap_dict = alberty.cid2pmap_dict
grad.deterministic_hill_climb(max_i=200)
grad.save_energies(grad.gc.comm, "gradient_cid2prm")
grad.verify_results("gradient_deterministic")
elif False:
# Run the gradient ascent algorithm, where the starting point arbitrary (predict all of the NIST compounds)
grad = GradientAscent(gc)
grad.load_nist_data(nist, skip_missing_reactions=False)
print "Training %d compounds using %d reactions: " % (len(
grad.cid2pmap_dict.keys()), len(grad.data))
grad.hill_climb(max_i=20000)
grad.save_energies(grad.gc.comm, "gradient_cid2prm")
grad.verify_results("gradient3")
elif False: # Use Alberty's table from (Mathematica 2006) to calculate the dG0 of all possible reactions in KEGG
grad = GradientAscent(gc)
grad.cid2pmap_dict = alberty.cid2pmap_dict
(pH, I, T) = (7, 0, 300)
counter = 0
for rid in grad.kegg.get_all_rids():
sparse_reaction = grad.kegg.rid2sparse_reaction(rid)
try:
dG0 = grad.reaction_to_dG0(sparse_reaction, pH, I, T)
print "R%05d: dG0_r = %.2f [kJ/mol]" % (rid, dG0)
counter += 1
except MissingCompoundFormationEnergy as e:
#print "R%05d: missing formation energy of C%05d" % (rid, e.cid)
pass
print "Managed to calculate the dG0 of %d reactions" % counter
elif False:
util._mkdir("../res/nist/fig")
csv_writer = csv.writer(open("../res/nist/pseudoisomers.csv", "w"))
cid_set = set()
for row in nist.data:
sparce_reaction = row['sparse']
cid_set.update(sparce_reaction.keys())
html_writer.write("<table border=1>\n")
for cid in sorted(list(cid_set)):
html_writer.write(" <tr><td>C%05d</td><td>%s</td><td>" %
(cid, grad.kegg.cid2name(cid)))
try:
mol = grad.kegg.cid2mol(cid)
img_fname = '../res/nist/fig/C%05d.png' % cid
html_writer.embed_img(img_fname, "C%05d" % cid)
mol.draw(show=False, filename=img_fname)
except AssertionError as e:
html_writer.write("WARNING: cannot draw C%05d - %s" %
(cid, str(e)))
except KeggParseException as e:
html_writer.write("WARNING: cannot draw C%05d - %s" %
(cid, str(e)))
html_writer.write("</td><td>")
if (cid in alberty.cid2pmap_dict):
for (nH, z) in alberty.cid2pmap_dict[cid].keys():
html_writer.write("(nH=%d, z=%d)<br>" % (nH, z))
csv_writer.writerow((cid, nH, z))
else:
nH = grad.kegg.cid2num_hydrogens(cid)
z = grad.kegg.cid2charge(cid)
html_writer.write("unknown pseudoisomers<br>")
html_writer.write("(nH=%d, z=%d)" % (nH, z))
csv_writer.writerow((cid, nH, z))
html_writer.write("</td></tr>\n")
html_writer.write("</table>\n")
html_writer.close()
continue
if self.override_pMg or self.override_I or self.override_T:
nist_row_copy = nist_row_data.Clone()
if self.override_pMg:
nist_row_copy.pMg = self.override_pMg
if self.override_I:
nist_row_copy.I = self.override_I
if self.override_T:
nist_row_copy.T = self.override_T
rows.append(nist_row_copy)
else:
rows.append(nist_row_data)
return rows
def GetUniqueReactionSet(self):
return set([row.reaction for row in self.data])
if __name__ == '__main__':
#logging.getLogger('').setLevel(logging.DEBUG)
_mkdir("../res/nist")
html_writer = HtmlWriter("../res/nist/statistics.html")
nist = Nist()
fp = open('../res/nist_kegg_ids.txt', 'w')
for cid in nist.GetAllCids():
fp.write("C%05d\n" % cid)
fp.close()
nist.AnalyzeStats(html_writer)
nist.AnalyzeConnectivity(html_writer)
html_writer.close()
continue
if self.override_pMg or self.override_I or self.override_T:
nist_row_copy = nist_row_data.Clone()
if self.override_pMg:
nist_row_copy.pMg = self.override_pMg
if self.override_I:
nist_row_copy.I = self.override_I
if self.override_T:
nist_row_copy.T = self.override_T
rows.append(nist_row_copy)
else:
rows.append(nist_row_data)
return rows
def GetUniqueReactionSet(self):
return set([row.reaction for row in self.data])
if __name__ == '__main__':
#logging.getLogger('').setLevel(logging.DEBUG)
_mkdir("../res/nist")
html_writer = HtmlWriter("../res/nist/statistics.html")
nist = Nist()
fp = open('../res/nist_kegg_ids.txt', 'w')
for cid in nist.GetAllCids():
fp.write("C%05d\n" % cid)
fp.close()
nist.AnalyzeStats(html_writer)
nist.AnalyzeConnectivity(html_writer)
html_writer.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 AnalyzeConcentrationGradient(pathway_file,
output_prefix,
thermo,
conc_range,
cids=[],
pH=None):
compound_names = ','.join([thermo.kegg.cid2name(cid) for cid in cids])
pathway_list = KeggFile2PathwayList(pathway_file)
pathway_names = [entry for (entry, _) in pathway_list]
html_writer = HtmlWriter('%s.html' % output_prefix)
# run once just to make sure that the pathways are all working:
logging.info("testing all pathways with default concentrations")
data = GetAllOBDs(pathway_list,
html_writer,
thermo,
pH=pH,
section_prefix="test",
balance_water=True,
override_bounds={})
csv_output = csv.writer(open('%s.csv' % output_prefix, 'w'))
csv_output.writerow(['pH', '[' + compound_names + ']'] + pathway_names)
conc_vec = 10**(-ParseConcentrationRange(conc_range)
) # logarithmic scale between 10mM and 1nM
override_bounds = {}
obd_mat = []
for conc in conc_vec.flat:
for cid in cids:
override_bounds[cid] = (conc, conc)
logging.info("[%s] = %.1e M" % (compound_names, conc))
data = GetAllOBDs(pathway_list,
html_writer=None,
thermo=thermo,
pH=pH,
section_prefix="",
balance_water=True,
override_bounds=override_bounds)
obds = [d['OBD'] for d in data]
obd_mat.append(obds)
csv_output.writerow([data[0]['pH'], conc] + obds)
obd_mat = np.matrix(
obd_mat) # rows are pathways and columns are concentrations
fig = plt.figure(figsize=(6, 6), dpi=90)
colormap = color.ColorMap(pathway_names)
for i, name in enumerate(pathway_names):
plt.plot(conc_vec,
obd_mat[:, i],
'-',
color=colormap[name],
figure=fig)
plt.title("OBD vs. [%s]" % (compound_names), figure=fig)
plt.xscale('log')
plt.ylim(ymin=0)
plt.xlabel('[%s] (in M)' % compound_names, figure=fig)
plt.ylabel('Optimized Distributed Bottleneck [kJ/mol]', figure=fig)
plt.legend(pathway_names)
html_writer.write('<h2>Summary figure</h1>\n')
html_writer.embed_matplotlib_figure(fig)
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()
def AnalyzePHGradient(pathway_file, output_prefix, thermo, conc_range):
pathway_list = KeggFile2PathwayList(pathway_file)
pathway_names = [entry for (entry, _) in pathway_list]
html_writer = HtmlWriter('%s.html' % output_prefix)
# run once just to make sure that the pathways are all working:
logging.info("testing all pathways with default pH")
data = GetAllOBDs(pathway_list,
html_writer,
thermo,
pH=None,
section_prefix="test",
balance_water=True,
override_bounds={})
csv_output = csv.writer(open('%s.csv' % output_prefix, 'w'))
csv_output.writerow(['pH'] + pathway_names)
util._mkdir(output_prefix)
shadow_csvs = {}
for d in data:
path = '%s/%s.csv' % (output_prefix, d['entry'])
shadow_csvs[d['entry']] = csv.writer(open(path, 'w'))
shadow_csvs[d['entry']].writerow(['pH'] + d['rids'])
pH_vec = ParseConcentrationRange(conc_range)
obd_mat = []
for pH in pH_vec.flat:
logging.info("pH = %.1f" % (pH))
data = GetAllOBDs(pathway_list,
html_writer=None,
thermo=thermo,
pH=pH,
section_prefix="",
balance_water=True,
override_bounds={})
obds = [d['OBD'] for d in data]
obd_mat.append(obds)
csv_output.writerow([data[0]['pH']] + obds)
for d in data:
if type(d['reaction prices']) != types.FloatType:
prices = list(d['reaction prices'].flat)
shadow_csvs[d['entry']].writerow([pH] + prices)
obd_mat = np.matrix(
obd_mat) # rows are pathways and columns are concentrations
fig = plt.figure(figsize=(6, 6), dpi=90)
colormap = color.ColorMap(pathway_names)
for i, name in enumerate(pathway_names):
plt.plot(pH_vec, obd_mat[:, i], '-', color=colormap[name], figure=fig)
plt.title("OBD vs. pH", figure=fig)
plt.ylim(0, np.max(obd_mat.flat))
plt.xlabel('pH', figure=fig)
plt.ylabel('Optimized Distributed Bottleneck [kJ/mol]', figure=fig)
plt.legend(pathway_names)
html_writer.write('<h2>Summary figure</h1>\n')
html_writer.embed_matplotlib_figure(fig)
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()
