def fluxreport(fluxdict,
expfluxdict=load.ExpFluxesfromXML('expdata.xml', 'Perrenoud',
'Batch', 'aerobe'),
exp_errordict=load.ExpErrorsfromXML('expdata.xml', 'Perrenoud',
'Batch', 'aerobe')):
fluxdictcopy = copy.deepcopy(fluxdict)
for key in fluxdictcopy:
if key not in expfluxdict:
del fluxdict[key]
diff_dict = {key: (expfluxdict[key] - fluxdict[key]) for key in fluxdict}
#bug here: KeyError: '26'
#uncertfraction = difference / experrors
uncertfrac_dict = {
key: (np.divide(diff_dict[key], exp_errordict[key]))
for key in fluxdict
}
textheader = [
"Ex rxn #", "Com. flux", "Exp flux", "Diff.", "Exp. uncert.",
"Diff/uncert."
]
outsidefluxes = 0
for key in uncertfrac_dict:
if (abs(uncertfrac_dict[key]) >
1) and (~np.isinf(abs(uncertfrac_dict[key]))):
outsidefluxes += 1
import datetime
now = datetime.datetime.now()
print("Flux report generated at:", now)
print("Solver:")
print("Model:")
print("Objective:")
print("Constraints:")
print("Number of computed fluxes outside experimental uncertainty bounds:",
outsidefluxes)
#print 'fluxdict:',fluxdict #DEBUG
#print 'expfluxdict:',expfluxdict #DEBUG
print("%15s %15s %15s %15s %15s %15s" % tuple(textheader))
#for key in fluxdict:
#print key #DEBUG
#print fluxdict[key]
#print expfluxdict[key]
#print diff_dict[key]
#print exp_errordict[key]
#print uncertfrac_dict[key]
#print uncertfrac_dict
#print "%s %15.1f %15.1f %15.1f %15.1f %15.1f" % (key,float(fluxdict[key]),float(expfluxdict[key]),float(diff_dict[key]),float(exp_errordict[key]),float(uncertfrac_dict[key]))
#Commenting out above line for the moment, as not sure how the Pythn 3 syntax for this is.
return diff_dict
def compdistdict(fluxdict, expdata=None, options=None, sense=None):
if expdata == None:
expdata = load.ExpFluxesfromXML('expdata.xml', 'Perrenoud', 'Batch',
'aerobe')
fluxvector = []
fluxvalues = []
for key in fluxdict:
if (key in expdata):
fluxvector.append(fluxdict[key])
fluxvalues.append(float(expdata[key]))
dist = np.linalg.norm(np.array(fluxvector) - np.array(fluxvalues))
return dist
def compdist(fluxvector,
expdata=None,
options=None,
sense=None,
extract=True,
rmap='Default'):
rmap = load.ReactionMapfromXML('reactionmaps.xml', 'Perrenoud', 'SCHUETZR')
if extract:
fluxvector = extractflux.extractfluxvector(fluxvector, rmap)
if expdata == None:
expdata = load.ExpFluxesfromXML('expdata.xml',
'Perrenoud',
'Batch',
'aerobe',
vector=True)
dist = np.linalg.norm(np.array(fluxvector) - np.array(expdata))
return dist
def compdistcomplete(rawfluxvector,model, debug = False):
if debug:
print('compdistcomplete: Calculating distance...')
import loadData as load
import extractflux
#print 'Loading experimental data...'
expdata = load.ExpFluxesfromXML('expdata.xml','Perrenoud','Batch','aerobe')
#print 'Loading reaction map...'
rmap = load.ReactionMapfromXML('reactionmaps.xml','Perrenoud','SCHUETZR')
tup = zip([reaction.id for reaction in model.reactions],rawfluxvector,) #
rawfluxdict = {reactionid:fluxvalue for reactionid,fluxvalue in tup}
extractfluxdict = extractflux.extractfluxdict(rawfluxdict,rmap)
fluxvector = []
fluxvalues = []
for key in extractfluxdict:
if (key in expdata):
fluxvector.append(extractfluxdict[key])
fluxvalues.append(float(expdata[key]))
dist = np.linalg.norm(np.array(fluxvector)-np.array(fluxvalues))
return dist
def printsplits(splits):
keys = splits.keys()
keys.sort(key=int)
for key in keys:
print key,'%.2f' %splits[key]
if __name__ == "__main__":
import loadData as load
from cobra.io.sbml import create_cobra_model_from_sbml_file
import extractflux as extract
expfluxes = load.ExpFluxesfromXML('expdata.xml','Perrenoud','Batch','aerobe')
splitmap = load.SplitMapfromXML('reactionmaps.xml','SCHUETZR','EXPDATA')
splits = computesplits(expfluxes,splitmap)
print 'Experimental splits:'
printsplits(splits)
splitmap = load.SplitMapfromXML('reactionmaps.xml','SCHUETZR','SCHUETZR')
cobramodel = create_cobra_model_from_sbml_file('../SBML/SCHUETZR.xml')
cobramodel.optimize(solver='gurobi')
compfluxes = extract.vectorToDict(cobramodel.solution.x,cobramodel)
compsplits = computesplits(compfluxes,splitmap)
print 'SCHUETZR FBA result splits:'
def optreqanalysis(modeldicts,
objectives,
experiments,
steps=10,
makeplots=True,
expfluxdict="Default",
plotter="easyviz"):
'''Function for evaluating how the minimally achievable distance between a flux solution and a set of experimental fluxes
changes when varying the required relative FBA objective-optimality of the flux solution.
syntax: optreqanalysis(modeldicts,objectives,experiments,steps = 10,makeplots = True,expfluxdict)
arguments:
modeldicts: An array of python dictionaries with the following fields:
modelobject: A CobraPy model object for the model in question.
reactionmap: A reaction map array mapping between model reactions and experimental reactions for the model and experiment in question.
objectives: A list of objectives to be used in the analysis.
experiments: A list of experiments to be used in the analysis
steps: The granularity of the analysis and produced plots.
makeplots: Whether plots should be produced or not
expfluxdict: A python dictionary with experiment-ids as keys and the corresponding flux value arrays as values.
'''
for objective in objectives:
pass #Placeholder
for experiment in experiments:
pass #Placeholder
if expfluxdict == "Default": #This section should be rewritten to use an "expflux" dict.
expdata = scipy.io.loadmat('expdata.mat') #load experimentaldata
perrenoud = expdata['expdata']['perrenoud']
fluxvalarray = perrenoud[0][0][0][0][0][0][0][0][0][0][0][0][0][0]
fluxvalues = [row[0] for row in fluxvalarray
] #expdata.perrenoud.abs.batch.aerobe.fluxvalues
expfluxdict = load.ExpFluxesfromXML('expdata.xml', 'Perrenoud',
'Batch', 'aerobe')
resultlist = []
for modeldict in modeldicts:
cobramodel = modeldict['modelobject']
#print 'modeldict:',modeldict
reactionmap = modeldict['reactionmap']
#print 'cobramodel:',cobramodel
#print type(cobramodel)
optreqs = []
for i in range(steps + 1):
optreqs.append(float(i) / steps)
results = []
#print 'opreqs:',optreqs
#q = raw_input('Continue?')
for optreq in optreqs:
optimizedmodel = QPmindist(cobramodel, fluxvalues, reactionmap,
optreq)
results.append(math.sqrt(optimizedmodel.ObjVal))
resultlist.append(results)
#Using easyviz
#ev.plot(optreqs,results)
#ev.hold()
#Using prettyplotlib
#fig, ax = plt.subplots(1)
#ppl.scatter(ax, x, y)
#ppl.plot(optreqs,results)
return resultlist