def export_dataStage02IsotopomerFluxMap_js(self,analysis_id_I,simulation_id_I = None,data_dir_I="tmp"):
'''Export flux map for viewing'''
MFAmethods = MFA_methods();
# Get the simulation information
if simulation_id_I:
simulation_id = simulation_id_I;
else:
simulation_ids = [];
simulation_ids = self.get_simulationID_analysisID_dataStage02IsotopomerAnalysis(analysis_id_I);
if not simulation_ids:
print('No simulation found for the analysis_id ' + analysis_id_I);
elif len(simulation_ids)>1:
print('More than 1 simulation found for the analysis_id ' + analysis_id_I);
simulation_id_I = simulation_ids[0];
else:
simulation_id_I = simulation_ids[0];
# Get the flux information
flux = [];
flux_tmp = [];
#flux = self.get_rowsEscherFluxList_simulationID_dataStage02IsotopomerfittedNetFluxes(simulation_id_I);
flux_tmp = self.get_rows_simulationID_dataStage02IsotopomerfittedNetFluxes(simulation_id_I);
for i,row in enumerate(flux_tmp):
observable = MFAmethods.check_observableNetFlux(row['flux'],row['flux_lb'],row['flux_ub']);
if not row['flux'] is None and row['flux']!=0.0 and np.abs(row['flux']) < 10.0:
flux_tmp[i]['simulation_dateAndTime'] = self.convert_datetime2string(row['simulation_dateAndTime']);
#flux_tmp[i]['flux_units'] = self.remove_jsRegularExpressions(row['flux_units']);
if observable: flux_tmp[i]['observable'] = 'Yes';
else: flux_tmp[i]['observable'] = 'No';
flux.append(flux_tmp[i]);
elif row['flux']==0.0 and np.abs(np.mean([row['flux_lb'],row['flux_ub']]))<10.0:
flux_tmp[i]['simulation_dateAndTime'] = self.convert_datetime2string(row['simulation_dateAndTime']);
#flux_tmp[i]['flux_units'] = self.remove_jsRegularExpressions(row['flux_units']);
#flux_tmp[i]['flux'] = np.mean([row['flux_lb'],row['flux_ub']]);
if observable: flux_tmp[i]['observable'] = 'Yes';
else: flux_tmp[i]['observable'] = 'No';
flux.append(flux_tmp[i]);
# Get the map information
map = [];
map = self.get_rows_modelID_modelsEschermaps('iJO1366');
# dump chart parameters to a js files
data1_keys = ['simulation_id','rxn_id','simulation_dateAndTime','flux_units','observable'
];
data1_nestkeys = ['simulation_id'];
data1_keymap = {'values':'flux','key':'rxn_id'};
data2_keys = ['model_id','eschermap_id'
];
data2_nestkeys = ['model_id'];
data2_keymap = {'data':'eschermap_json'};
# make the data object
dataobject_O = [{"data":flux,"datakeys":data1_keys,"datanestkeys":data1_nestkeys},
{"data":map,"datakeys":data2_keys,"datanestkeys":data2_nestkeys}];
# make the tile parameter objects
formtileparameters1_O = {'tileheader':'Filter menu','tiletype':'html','tileid':"filtermenu1",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-6"};
formparameters1_O = {'htmlid':'filtermenuform1',"htmltype":'form_01',"formsubmitbuttonidtext":{'id':'submit1','text':'submit'},"formresetbuttonidtext":{'id':'reset1','text':'reset'},"formupdatebuttonidtext":{'id':'update1','text':'update'}};
formtileparameters1_O.update(formparameters1_O);
formtileparameters2_O = {'tileheader':'Filter menu','tiletype':'html','tileid':"filtermenu2",'rowid':"row1",'colid':"col2",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-6"};
formparameters2_O = {'htmlid':'filtermenuform2',"htmltype":'form_01',"formsubmitbuttonidtext":{'id':'submit2','text':'submit'},"formresetbuttonidtext":{'id':'reset2','text':'reset'},"formupdatebuttonidtext":{'id':'update2','text':'update'}};
formtileparameters2_O.update(formparameters2_O);
htmlparameters_O = {"htmlkeymap":[data1_keymap,data2_keymap],
'htmltype':'escher_01','htmlid':'html1',
'escherdataindex':{"reactiondata":0,"mapdata":1},
'escherembeddedcss':None,
'escheroptions':None};
htmltileparameters_O = {'tileheader':'Escher map','tiletype':'html','tileid':"tile1",'rowid':"row2",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
htmltileparameters_O.update(htmlparameters_O);
tableparameters_O = {"tabletype":'responsivetable_01',
'tableid':'table1',
"tablefilters":None,
"tableclass":"table table-condensed table-hover",
'tableformtileid':'filtermenu1','tableresetbuttonid':'reset1','tablesubmitbuttonid':'submit1'};
tabletileparameters_O = {'tileheader':'Flux precision','tiletype':'table','tileid':"tile2",'rowid':"row3",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
tabletileparameters_O.update(tableparameters_O);
parametersobject_O = [formtileparameters1_O,formtileparameters2_O,htmltileparameters_O,tabletileparameters_O];
tile2datamap_O = {"filtermenu1":[0],"filtermenu2":[1],"tile1":[0,1],"tile2":[0]};
filtermenuobject_O = [{"filtermenuid":"filtermenu1","filtermenuhtmlid":"filtermenuform1",
"filtermenusubmitbuttonid":"submit1","filtermenuresetbuttonid":"reset1",
"filtermenuupdatebuttonid":"update1"},{"filtermenuid":"filtermenu2","filtermenuhtmlid":"filtermenuform2",
"filtermenusubmitbuttonid":"submit2","filtermenuresetbuttonid":"reset2",
"filtermenuupdatebuttonid":"update2"}];
# dump the data to a json file
ddtutilities = ddt_container(parameters_I = parametersobject_O,data_I = dataobject_O,tile2datamap_I = tile2datamap_O,filtermenu_I = filtermenuobject_O);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = ddtutilities.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(ddtutilities.get_allObjects());
def execute_findNetFluxSignificantDifferences(self,analysis_id_I, criteria_I = 'flux_lb/flux_ub',
simulation_ids_I=[],simulation_dateAndTimes_I = [],
rxn_ids_I = [],flux_units_I = [],
control_simulation_id_I=None,
control_simulation_dateAndTime_I=None,
redundancy_I=False,
observable_only_I=False):
"""Find fluxes that are significantly different
Input:
analysis_id_I = string,
criteria_I = string, flux_lb/flux_ub: use flux_lb and flux_ub to determine significance (default)
flux_mean/flux_stdev: use the flux_mean and flux_stdev to determine significance
control_simulation_id_I = string, simulation_id to compare all other simulation_ids to
simulation_dateAndTime_I = string, simulation_dateAndTime to compare all other simulation_ids to
redundancy_I = boolean, if true, all values with be compared, if false (default), only unique comparisons will be made
observable_only_I = boolean, if true, only observable fluxes will be compared, if false (default), observable and unobservable fluxes will be compared
"""
mfamethods = MFA_methods();
data_O = [];
print('executing findNetFluxSignificantDifferences...')
# get the simulation_id and simulation_id dateAndTimes
if simulation_ids_I and simulation_dateAndTimes_I:
simulation_ids = simulation_ids_I;
simulation_dateAndTimes = simulation_dateAndTimes_I;
else:
simulation_ids = [];
simulation_ids_unique = [];
simulation_dateAndTimes = [];
# get the simulation unique ids
simulation_ids_unique = self.get_simulationID_analysisID_dataStage02IsotopomerAnalysis(analysis_id_I);
for simulation_id in simulation_ids_unique:
# get the simulation dateAndTimes
simulation_dateAndTimes_tmp = []
simulation_dateAndTimes_tmp = self.get_simulationDateAndTimes_simulationID_dataStage02IsotopomerfittedNetFluxes(simulation_id);
simulation_ids_tmp = [simulation_id for x in simulation_dateAndTimes_tmp];
simulation_dateAndTimes.extend(simulation_dateAndTimes_tmp)
simulation_ids.extend(simulation_ids_tmp)
if control_simulation_id_I and control_simulation_dateAndTime_I:
index = simulation_ids.index(control_simulation_id_I);
value = simulation_ids.pop(index);
simulation_ids.insert(0, value);
control_simulation_dateAndTime_I = self.convert_string2datetime(control_simulation_dateAndTime_I);
index = simulation_dateAndTimes.index(control_simulation_dateAndTime_I);
value = simulation_dateAndTimes.pop(index)
simulation_dateAndTimes.insert(0, value);
for simulation_cnt_1, simulation_id_1 in enumerate(simulation_ids):
print("calculating netFluxDifferences for simulation_id " + simulation_id_1);
# check for control
if control_simulation_id_I and control_simulation_dateAndTime_I and simulation_cnt_1>0:
break;
#prevents redundancy and
if simulation_cnt_1+1 >= len(simulation_ids):
break;
# get the units
if flux_units_I:
flux_units = flux_units_I;
else:
flux_units = self.get_fluxUnits_simulationIDAndSimulationDateAndTime_dataStage02IsotopomerfittedNetFluxes(simulation_id_1,simulation_dateAndTimes[simulation_cnt_1])
for flux_unit in flux_units:
print("calculating netFluxDifferences for flux_units " + flux_unit);
# get the rxn_ids
if rxn_ids_I:
rxn_ids = rxn_ids_I;
else:
rxn_ids = [];
rxn_ids = self.get_rxnIDs_simulationIDAndSimulationDateAndTimeAndFluxUnits_dataStage02IsotopomerfittedNetFluxes(simulation_id_1,simulation_dateAndTimes[simulation_cnt_1],flux_unit);
for rxn_id in rxn_ids:
print("calculating netFluxDifferes for rxn_id " + rxn_id);
# get simulation_id_1 flux data
flux_1,flux_stdev_1,flux_lb_1,flux_ub_1,flux_units_1=None,None,None,None,None;
flux_1,flux_stdev_1,flux_lb_1,flux_ub_1,flux_units_1=self.get_flux_simulationIDAndSimulationDateAndTimeAndFluxUnitsAndRxnID_dataStage02IsotopomerfittedNetFluxes(simulation_id_1,simulation_dateAndTimes[simulation_cnt_1],flux_unit,rxn_id);
if not mfamethods.check_criteria(flux_1,flux_stdev_1,flux_lb_1,flux_ub_1, criteria_I):
continue;
if redundancy_I: list_2 = simulation_ids;
else: list_2 = simulation_ids[simulation_cnt_1+1:];
if observable_only_I:
observable_1 = mfamethods.check_observableNetFlux(flux_1,flux_lb_1,flux_ub_1)
if not observable_1: continue;
for cnt,simulation_id_2 in enumerate(list_2): #prevents redundancy
if redundancy_I: simulation_cnt_2 = cnt;
else: simulation_cnt_2 = simulation_cnt_1+cnt+1;
if simulation_cnt_2 == simulation_cnt_1:
continue;
# simulation_id_2 flux_data
flux_2,flux_stdev_2,flux_lb_2,flux_ub_2,flux_units_2=None,None,None,None,None;
flux_2,flux_stdev_2,flux_lb_2,flux_ub_2,flux_units_2=self.get_flux_simulationIDAndSimulationDateAndTimeAndFluxUnitsAndRxnID_dataStage02IsotopomerfittedNetFluxes(simulation_id_2,simulation_dateAndTimes[simulation_cnt_2],flux_unit,rxn_id);
if not mfamethods.check_criteria(flux_2,flux_stdev_2,flux_lb_2,flux_ub_2, criteria_I):
continue;
if observable_only_I:
observable_2 = mfamethods.check_observableNetFlux(flux_2,flux_lb_2,flux_ub_2);
if not observable_2: continue;
flux_diff,flux_distance,fold_change,significant = None,None,None,False;
flux_diff,flux_distance,fold_change,significant = mfamethods.calculate_fluxDifference(flux_1,flux_stdev_1,flux_lb_1,flux_ub_1,flux_units_1,
flux_2,flux_stdev_2,flux_lb_2,flux_ub_2,flux_units_2,
criteria_I = criteria_I);
# record the data
data_O.append({
'analysis_id':analysis_id_I,
'simulation_id_1':simulation_id_1,
'simulation_dateAndTime_1':simulation_dateAndTimes[simulation_cnt_1],
'simulation_id_2':simulation_id_2,
'simulation_dateAndTime_2':simulation_dateAndTimes[simulation_cnt_2],
'rxn_id':rxn_id,
'flux_difference':flux_diff,
'significant':significant,
'significant_criteria':criteria_I,
'flux_units':flux_unit,
'fold_change_geo':fold_change,
'flux_distance':flux_distance,
'used_':True,
'comment_':None});
# add data to the database
self.add_data_stage02_isotopomer_fittedNetFluxDifferences(data_O);
def export_dataStage02IsotopomerFittedNetFluxes_js(self,analysis_id_I = None,
simulation_ids_I = [],
bullet_chart_I = True,
data_dir_I="tmp"):
'''Plot the flux precision for a given set of simulations and a given set of reactions
Input:
analysis_id_I = string, analysis id
Optional Input:
simulation_ids_I = [] of strings, simulation_ids in a specific order
bullet_chart_I = True: show the flux estimation +/- StDev
False: show the 95% confidence invervals and flux estimation +/- StDev
'''
MFAmethods = MFA_methods();
#Input:
# analysis_id_I or
# simulation_ids_I
if simulation_ids_I:
simulation_ids = simulation_ids_I;
else:
simulation_ids = [];
simulation_ids = self.get_simulationID_analysisID_dataStage02IsotopomerAnalysis(analysis_id_I);
data_O =[];
for simulation_id in simulation_ids:
# get the flux information for each simulation
flux_data = [];
flux_data = self.get_rows_simulationID_dataStage02IsotopomerfittedNetFluxes(simulation_id);
#min_flux,max_flux = None,None;
#min_flux,max_flux = self.get_fluxMinAndMax_simulationID_dataStage02IsotopomerfittedNetFluxes(simulation_id)
for i,row in enumerate(flux_data):
observable = MFAmethods.check_observableNetFlux(row['flux'],row['flux_lb'],row['flux_ub']);
if not row['flux'] is None:
row['simulation_dateAndTime'] = self.convert_datetime2string(row['simulation_dateAndTime']);
#row['flux_units'] = row['flux_units'].replace('*','x');
row['flux_lb_stdev'] = row['flux'] - row['flux_stdev'];
row['flux_ub_stdev'] = row['flux'] + row['flux_stdev'];
row['flux_mean'] = np.mean([row['flux_lb'],row['flux_ub']]);
if observable: row['observable'] = 'Yes';
else: row['observable'] = 'No';
data_O.append(row);
#if not row['flux'] is None and observable:
# row['simulation_dateAndTime'] = self.convert_datetime2string(row['simulation_dateAndTime']);
# row['flux_units'] = row['flux_units'].replace('*','x');
# row['flux_lb_stdev'] = row['flux'] - row['flux_stdev'];
# row['flux_ub_stdev'] = row['flux'] + row['flux_stdev'];
# row['flux_mean'] = np.mean([row['flux_lb'],row['flux_ub']]);
# data_O.append(row);
#elif not row['flux'] is None and not observable:
# row['simulation_dateAndTime'] = self.convert_datetime2string(row['simulation_dateAndTime']);
# row['flux_units'] = row['flux_units'].replace('*','x');
# row['flux_lb'] = None;
# row['flux_ub'] = None;
# row['flux_mean'] = None;
# data_O.append(row);
#elif row['flux']==0.0 and observable:
# row['simulation_dateAndTime'] = self.convert_datetime2string(row['simulation_dateAndTime']);
# row['flux_units'] = row['flux_units'].replace('*','x');
# row['flux_lb_stdev'] = 0.0;
# row['flux_ub_stdev'] = 0.0;
# row['flux_mean'] = np.mean([row['flux_lb'],row['flux_ub']]);
# data_O.append(row);
#elif row['flux']==0.0 and not observable:
# row['simulation_dateAndTime'] = self.convert_datetime2string(row['simulation_dateAndTime']);
# row['flux_units'] = row['flux_units'].replace('*','x');
# row['flux_lb'] = None;
# row['flux_ub'] = None;
# #row['flux_mean'] = None;
# row['flux_lb_stdev'] = None;
# row['flux_ub_stdev'] = None;
# row['flux']=None;
# data_O.append(row);
elif row['flux']==0.0:
row['simulation_dateAndTime'] = self.convert_datetime2string(row['simulation_dateAndTime']);
#row['flux_units'] = row['flux_units'].replace('*','x');
row['flux_lb_stdev'] = 0.0;
row['flux_ub_stdev'] = 0.0;
row['flux_mean'] = np.mean([row['flux_lb'],row['flux_ub']]);
if observable: row['observable'] = 'Yes';
else: row['observable'] = 'No';
data_O.append(row);
# dump chart parameters to a js files
data1_keys = ['simulation_id','rxn_id','simulation_dateAndTime','flux_units','observable'
];
data1_nestkeys = ['rxn_id'];
if bullet_chart_I:
data1_keymap = {'xdata':'rxn_id',
'ydatamean':'flux',
'ydatalb':'flux_lb_stdev',
'ydataub':'flux_ub_stdev',
'serieslabel':'simulation_id',
'featureslabel':'rxn_id'};
else:
data1_keymap = {'xdata':'rxn_id',
'ydatamean':'flux',
#'ydata':'flux_mean',
'ydatalb':'flux_lb',
'ydataub':'flux_ub',
#'ydatamin':'min',
#'ydatamax':'max',
'ydataiq1':'flux_lb_stdev',
'ydataiq3':'flux_ub_stdev',
'ydatamedian':'flux',
'serieslabel':'simulation_id',
'featureslabel':'rxn_id'};
# make the data object
dataobject_O = [{"data":data_O,"datakeys":data1_keys,"datanestkeys":data1_nestkeys}];
# make the tile parameter objects
formtileparameters_O = {'tileheader':'Filter menu','tiletype':'html','tileid':"filtermenu1",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
formparameters_O = {'htmlid':'filtermenuform1',"htmltype":'form_01',"formsubmitbuttonidtext":{'id':'submit1','text':'submit'},"formresetbuttonidtext":{'id':'reset1','text':'reset'},"formupdatebuttonidtext":{'id':'update1','text':'update'}};
formtileparameters_O.update(formparameters_O);
svgparameters_O = {"svgtype":'boxandwhiskersplot2d_02',"svgkeymap":[data1_keymap,data1_keymap],
'svgid':'svg1',
"svgmargin":{ 'top': 50, 'right': 350, 'bottom': 50, 'left': 50 },
"svgwidth":750,"svgheight":350,
"svgx1axislabel":"rxn_id","svgy1axislabel":"flux",
'svgformtileid':'filtermenu1','svgresetbuttonid':'reset1','svgsubmitbuttonid':'submit1'};
svgtileparameters_O = {'tileheader':'Flux precision','tiletype':'svg','tileid':"tile2",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
svgtileparameters_O.update(svgparameters_O);
tableparameters_O = {"tabletype":'responsivetable_01',
'tableid':'table1',
"tablefilters":None,
"tableclass":"table table-condensed table-hover",
'tableformtileid':'filtermenu1','tableresetbuttonid':'reset1','tablesubmitbuttonid':'submit1'};
tabletileparameters_O = {'tileheader':'Flux precision','tiletype':'table','tileid':"tile3",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
tabletileparameters_O.update(tableparameters_O);
parametersobject_O = [formtileparameters_O,svgtileparameters_O,tabletileparameters_O];
tile2datamap_O = {"filtermenu1":[0],"tile2":[0],"tile3":[0]};
# dump the data to a json file
ddtutilities = ddt_container(parameters_I = parametersobject_O,data_I = dataobject_O,tile2datamap_I = tile2datamap_O,filtermenu_I = None);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = ddtutilities.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(ddtutilities.get_allObjects());
