def export_listDict_csv(self,filename_O):
'''export a listDict to .csv
INPUT:
filename_O = string, name of the file
'''
export = base_exportData(self.listDict);
export.write_dict2csv(filename_O);
def export_visualizationProject_csv(self,project_id_I,filename_O):
'''export the visualization project to csv'''
data1_O = [];
data1_O = self.get_rows_projectID_visualizationProject(project_id_I);
io = base_exportData(data1_O);
io.write_dict2csv(filename_O);
def export_checkLLOQAndULOQ_csv(self,experiment_id_I,filename='checkLLOQAndULOQ.csv'):
'''export LLOQ and ULOQ check'''
#TODO change to export method for .csv and .js
print('export_checkLLOQAndULOQ...')
# query data for the view
check = [];
check = self.get_checkLLOQAndULOQ(experiment_id_I);
## create and populate the view
#for n in range(len(check)):
# if check[n]:
# row = data_stage01_quantification_checkLLOQAndULOQ(experiment_id_I,
# check[n]['sample_name'],
# check[n]['component_group_name'],
# check[n]['component_name'],
# check[n]['calculated_concentration'],
# check[n]['conc_units'],
# check[n]['correlation'],
# check[n]['lloq'],
# check[n]['uloq'],
# check[n]['points'],
# check[n]['used']);
# self.session.add(row);
#self.session.commit();
if check:
export = base_exportData(check);
export.write_dict2csv(filename);
else:
print("all components are within the lloq and uloq");
def export_dataStage01ReplicatesMI_csv(self, experiment_id_I, filename):
'''export dataStage01ReplicatesMI to csv file'''
# query the data
data = [];
data = self.get_data_experimentID_dataStage01ReplicatesMI(experiment_id_I);
# expand the data file:
sns = []
cgn = []
for d in data:
sns.append(d['sample_name_short']);
cgn.append(d['component_group_name']);
sns_sorted = sorted(set(sns))
cgn_sorted = sorted(set(cgn))
concentrations = []
for c in cgn_sorted:
row = ['NA' for r in range(len(sns_sorted))];
cnt = 0;
for s in sns_sorted:
for d in data:
if d['sample_name_short'] == s and d['component_group_name'] == c:
if d['calculated_concentration']:
row[cnt] = d['calculated_concentration'];
break;
cnt = cnt+1
concentrations.append(row);
# write concentrations to file
export = base_exportData(concentrations);
export.write_headerAndColumnsAndElements2csv(sns_sorted,cgn_sorted,filename);
def export_rows_experimentID_sample_csv(self, experiment_id_I,filename_O):
'''export rows of table sample by experiment_id'''
data_O = [];
data_O = self.get_rows_experimentID_sample('ALEsKOs01');
if data_O:
baseo = base_exportData(data_O);
else:
print('no rows found.');
def export_dataStage01Normalized_csv(self,filename,experiment_id_I=[],sample_name_I=[],
component_name_I=[],
calculated_concentration_units_I=[],
used__I=True):
'''export data_stage01_quantification_normalized to csv'''
print('export data_stage01_quantification_normalized to csv...')
# query data for the view
data = [];
if experiment_id_I:
for experiment_id in experiment_id_I:
if experiment_id_I and sample_name_I:
for sample_name in sample_name_I:
if experiment_id_I and sample_name_I and component_name_I:
for component_name in component_name_I:
if experiment_id_I and sample_name_I and component_name_I and calculated_concentration_units_I:
for ccu in calculated_concentration_units_I:
data_tmp = [];
data_tmp = self.get_rows_uniqueAndUsed_dataStage01QuantificationNormalized(experiment_id_I=experiment_id,
sample_name_I=sample_name,
component_name_I=component_name,
calculated_concentration_units_I=ccu,
used__I=used__I);
data.extend(data_tmp)
else:
data_tmp = [];
data_tmp = self.get_rows_uniqueAndUsed_dataStage01QuantificationNormalized(experiment_id_I=experiment_id,
sample_name_I=sample_name,
component_name_I=component_name,
calculated_concentration_units_I='%',
used__I=used__I);
data.extend(data_tmp);
else:
data_tmp = [];
data_tmp = self.get_rows_uniqueAndUsed_dataStage01QuantificationNormalized(experiment_id_I=experiment_id,
sample_name_I=sample_name,
component_name_I='%',
calculated_concentration_units_I='%',
used__I=used__I);
data.extend(data_tmp);
else:
data_tmp = [];
data_tmp = self.get_rows_uniqueAndUsed_dataStage01QuantificationNormalized(experiment_id_I=experiment_id,
sample_name_I='%',
component_name_I='%',
calculated_concentration_units_I='%',
used__I=used__I);
data.extend(data_tmp);
else:
data = self.get_rows_uniqueAndUsed_dataStage01QuantificationNormalized(experiment_id_I='%',
sample_name_I='%',
component_name_I='%',
calculated_concentration_units_I='%',
used__I=True);
if data:
export = base_exportData(data);
export.write_dict2csv(filename);
else:
print("no rows found");
def export_data_stage02_isotopomer_fittedExchangeFluxes_csv(self,simulation_ids_I,filename_O):
"""export data_stage02_isotopomer_fittedExchangeFluxes
INPUT:
simulation_id_I = [] of string, simulation_id
filename_O = string, filename for export"""
data_O = [];
for simulation_id in simulation_ids_I:
data_tmp =[];
data_tmp = self.get_rows_simulationID_dataStage02IsotopomerfittedExchangeFluxes(simulation_id);
data_O.extend(data_tmp);
if data_O:
io = base_exportData(data_O);
io.write_dict2csv(filename_O);
def export_dataStage01IsotopomerNormalized_csv(self, experiment_id_I, filename_O,
sample_name_abbreviation_I='%',
time_point_I='%',
scan_type_I='%',
met_id_I='%'):
'''export data_stage01_isotopomer_normalized to .csv'''
data = [];
data = self.get_rows_experimentIDAndSampleAbbreviationAndTimePointAndScanTypeAndMetID_dataStage01Normalized(experiment_id_I,sample_name_abbreviation_I,time_point_I,scan_type_I,met_id_I)
if data:
# write data to file
export = base_exportData(data);
export.write_dict2csv(filename_O);
def export_sampleStorage_csv(self, sample_ids_I, filename_O):
"""Export sample storage to .csv
INPUT:
sample_ids_I = [] of string, sample_id
filename_O = string, filename for export"""
data_O = [];
for sample_id in sample_ids_I:
data_tmp =[];
data_tmp = self.get_rows_sampleID_limsSampleStorage(sample_id);
data_O.extend(data_tmp);
if data_O:
io = base_exportData(data_O);
io.write_dict2csv(filename_O);
def export_mapGeneName2ModelReaction_csv(self,rows_I,
filename_O):
"""return the model reaction rows whose enzymes are produced by a given gene
INPUT:
rows_I = rows from data_stage02_physiology_modelReactions
OUTPUT:
filename_O = name of output file
rows from data_stage02_physiology_modelReactions in a .csv file
"""
if rows_I:
io = base_exportData(rows_I);
io.write_dict2csv(filename_O);
else:
print('no rows found');
def export_calibratorConcentrations_csv(self,filename,met_ids_I=[]):
'''export calibrator concentrations'''
data_O = [];
if met_ids_I:
met_ids = met_ids_I;
else:
met_ids = [];
met_ids = self.get_metIDs_calibratorConcentrations();
for met_id in met_ids:
rows = [];
rows = self.get_rows_metID_calibratorConcentrations(met_id);
data_O.extend(rows);
export = base_exportData(data_O);
export.write_dict2csv(filename);
def export_checkISMatch_csv(self,experiment_id_I,filename='checkISMatch.csv'):
'''check that the internal standard used in the data file
matches that of the calibration method'''
'''SELECT
experiment.id,
data_stage01_quantification_mqresultstable.sample_name,
data_stage01_quantification_mqresultstable.component_name,
data_stage01_quantification_mqresultstable.is_name,
quantitation_method.is_name
FROM
public.data_stage01_quantification_mqresultstable,
public.experiment,
public.quantitation_method
WHERE
experiment.id LIKE 'ibop_rbc02' AND
experiment.sample_name LIKE data_stage01_quantification_mqresultstable.sample_name AND
(data_stage01_quantification_mqresultstable.sample_type LIKE 'Unknown' OR
data_stage01_quantification_mqresultstable.sample_type LIKE 'Quality Control') AND
experiment.quantitation_method_id LIKE quantitation_method.id AND
quantitation_method.component_name LIKE data_stage01_quantification_mqresultstable.component_name AND
data_stage01_quantification_mqresultstable.used_ AND
NOT data_stage01_quantification_mqresultstable.is_ AND
data_stage01_quantification_mqresultstable.is_name NOT LIKE quantitation_method.is_name;'''
#TODO change to export method for .csv and .js
print('execute_checkISMatch...')
# query data for the view
check = [];
check = self.get_checkISMatch(experiment_id_I);
## create and populate the view
#for n in range(len(check)):
# if check[n]:
# row = data_stage01_quantification_checkISMatch(experiment_id_I,
# check[n]['sample_name'],
# check[n]['component_name'],
# check[n]['IS_name_samples'],
# check[n]['IS_name_calibrators']);
# self.session.add(row);
#self.session.commit();
if check:
print("IS mismatches found");
export = base_exportData(check);
export.write_dict2csv(filename);
else:
print("No IS mismatches found");
def export_data_stage02_isotopomer_measuredFluxes_csv(self,experiment_ids_I,model_ids_I,sample_name_abbreviations_I,filename_O):
"""export data_stage02_isotopomer_measuredFluxes
INPUT:
experiment_ids_I = [] of string, experiment_id
model_ids_I = [] of strings, model_id
sample_name_abbreviations_I = [] of string, sample_name_abbreviation
filename_O = string, filename for export
"""
data_O = [];
for experiment_id in experiment_ids_I:
for model_id in model_ids_I:
for sna in sample_name_abbreviations_I:
data_tmp =[];
data_tmp = self.get_rows_experimentIDAndModelIDAndSampleNameAbbreviation_dataStage02IsotopomerMeasuredFluxes(experiment_id,model_id,sna);
data_O.extend(data_tmp);
if data_O:
io = base_exportData(data_O);
io.write_dict2csv(filename_O);
def export_data_stage02_isotopomer_fittedExchangeFluxStatistics_csv(self,simulation_ids_I,filename_O,flux_units_I=[]):
"""export data_stage02_isotopomer_fittedExchangeFluxStatistics
INPUT:
simulation_id_I = [] of string, simulation_id
filename_O = string, filename for export"""
data_O = [];
for simulation_id in simulation_ids_I:
if flux_units_I:
for flux_units in flux_units_I:
data_tmp =[];
data_tmp = self.get_rows_simulationIDAndFluxUnits_dataStage02IsotopomerFittedExchangeFluxStatistics(simulation_id,flux_units);
data_O.extend(data_tmp);
else:
data_tmp =[];
data_tmp = self.get_rows_simulationID_dataStage02IsotopomerFittedExchangeFluxStatistics(simulation_id);
data_O.extend(data_tmp);
if data_O:
io = base_exportData(data_O);
io.write_dict2csv(filename_O);
def export_data_stage02_isotopomer_measuredFragments_csv(self,experiment_ids_I,sample_name_abbreviations_I,filename_O,time_points_I=[]):
"""export data_stage02_isotopomer_measuredFragments
INPUT:
experiment_ids_I = [] of string, experiment_id
sample_name_abbreviations_I = [] of string, sample_name_abbreviation
filename_O = string, filename for export
time_points_I = [] of strings, time_point"""
data_O = [];
for experiment_id in experiment_ids_I:
for sna in sample_name_abbreviations_I:
if time_points_I:
for tp in time_points_I:
data_tmp =[];
data_tmp = self.get_row_experimentIDAndSampleNameAbbreviationAndTimePoint_dataStage02IsotopomerMeasuredFragments(experiment_id,sna,tp);
data_O.extend(data_tmp);
else:
data_tmp =[];
data_tmp = self.get_row_experimentIDAndSampleNameAbbreviation_dataStage02IsotopomerMeasuredFragments(experiment_id,sna);
data_O.extend(data_tmp);
if data_O:
io = base_exportData(data_O);
io.write_dict2csv(filename_O);
def export_checkCVAndExtracelluar_averages_csv(self,experiment_id_I,filename,cv_threshold_I=20,extracellular_threshold_I=50):
'''check the CV and % Extracellular of the averages table
INPUT:
experiment_id_I = experiment_id
cv_threshold_I = float, % cv tolerance
extracellular_threshold_I = float, % extracellular threshold
'''
print('execute_checkCVAndExtracelluar_averages...')
# query data for the view
check = [];
check = self.get_checkCVAndExtracellular_averages(experiment_id_I,cv_threshold_I=cv_threshold_I,extracellular_threshold_I=extracellular_threshold_I);
## create and populate the view
#for n in range(len(check)):
# if check[n]:
# row = data_stage01_quantification_checkCVAndExtracellular_averages(check[n]['experiment_id'],
# check[n]['sample_name_abbreviation'],
# check[n]['component_group_name'],
# check[n]['time_point'],
# check[n]['component_name'],
# check[n]['n_replicates_broth'],
# check[n]['calculated_concentration_broth_average'],
# check[n]['calculated_concentration_broth_cv'],
# check[n]['n_replicates_filtrate'],
# check[n]['calculated_concentration_filtrate_average'],
# check[n]['calculated_concentration_filtrate_cv'],
# check[n]['n_replicates'],
# check[n]['calculated_concentration_average'],
# check[n]['calculated_concentration_cv'],
# check[n]['calculated_concentration_units'],
# check[n]['extracellular_percent'],
# check[n]['used']);
# self.session.add(row);
#self.session.commit();
if check:
export = base_exportData(check);
export.write_dict2csv(filename);
else:
print("all components are within the %CV and %Extracellular tolerance");
def export_allMetaboliteFragmentMappings_csv(filename_I='ms_method_fragments.csv',mode_I='-',msmethodtype_I='tuning'):
'''export all metabolite fragment mappings to .csv
INPUT:
filename_I = string, filename
mode_I = string, MS mode (default=-)
msmethodtype_I = string, ms_methodtype (default=tuning)
'''
mfautil = MFA_utilities();
#Query met_ids
met_ids = [];
met_ids = msmethodquery.get_metIDs_msModeAndMsMethodType('-','tuning');
data_O = [];
for met in met_ids:
# fragments
fragment_formulas = [];
parent,product=[],[];
parent,product= msmethodquery.get_precursorAndProductFormulas_metID(met,'-','tuning');
parent = list(set(parent));
fragment_formulas.extend(parent);
fragment_formulas.extend(product);
# fragment carbon mappings
frag_cmap = {};
frag_cmap = msmethodquery.get_precursorFormulaAndProductFormulaAndCMapsAndPositions_metID(met,'-','tuning');
for frag in fragment_formulas:
if frag_cmap[frag]['fragment'] is None: continue;
# combine into a structure
positions,elements = [],[];
positions,elements = mfautil.convert_fragmentAndElements2PositionAndElements(frag_cmap[frag]['fragment'],frag_cmap[frag]['fragment_elements']);
if positions:
tmp = {'met_id':met,'positions':positions,'elements':elements,'formula':frag};
data_O.append(tmp);
# export the data
if data_O:
export = base_exportData(data_O)
export.write_dict2csv(filename_I);
def export_checkCV_dilutions_csv(self,experiment_id_I,filename='checkCV_dilutions.csv'):
'''Export the dilutions table'''
#TODO change to export method for .csv and .js
print('execute_checkCV_dilutions...')
# query data for the view
check = [];
check = self.get_checkCV_dilutions(experiment_id_I);
## create and populate the view
#for n in range(len(check)):
# if check[n]:
# row = data_stage01_quantification_checkCV_dilutions(check[n]['experiment_id'],
# check[n]['sample_id'],
# check[n]['component_group_name'],
# check[n]['component_name'],
# check[n]['n_replicates'],
# check[n]['calculated_concentration_average'],
# check[n]['calculated_concentration_cv'],
# check[n]['calculated_concentration_units']);
# self.session.add(row);
#self.session.commit();
export = base_exportData(check);
export.write_dict2csv(filename);
def export_evidence(self, filename_O):
"""export evidence"""
io = base_exportData(self.evidence)
io.write_dict2csv(filename_O)
def export_validation(self, filename_O):
"""export validation"""
io = base_exportData(self.validation)
io.write_dict2csv(filename_O)
def export_mutations(self, filename_O):
"""export mutations"""
io = base_exportData(self.mutations)
io.write_dict2csv(filename_O)
def export_metadata(self, filename_O):
"""export metadata"""
io = base_exportData(self.metadata)
io.write_dict2csv(filename_O)
def export_genesFpkmTracking(self,filename_O):
"""export genesFpkmTracking"""
io = base_exportData(self.genesFpkmTracking);
io.write_dict2csv(filename_O);
def export_calibrationConcentrations(self, data, filename):
'''export calibration curve concentrations'''
# write calibration curve concentrations to file
export = base_exportData(data);
export.write_dict2csv(filename);
def export_amplificationAnnotations(self,filename_O):
"""export amplificationAnnotations"""
io = base_exportData(self.amplificationAnnotations);
io.write_dict2csv(filename_O);
def export_mutationsFiltered(self, filename_O):
"""export mutationsFiltered"""
io = base_exportData(self.mutationsFiltered)
io.write_dict2csv(filename_O)
def export_batchFile(self, data_I, header_I, filename):
export = base_exportData(data_I)
export.write_headersAndElements2txt(header_I,filename)
return;
def export_geneExpDiff(self, filename_O):
"""export geneExpDiff"""
io = base_exportData(self.geneExpDiff)
io.write_dict2csv(filename_O)
def export_coverageStats(self,filename_O):
"""export coverageStats"""
io = base_exportData(self.coverageStats);
io.write_dict2csv(filename_O);
def export_thermodynamicAnalysisComparison_csv(self,experiment_id_I,sample_name_abbreviation_base,
model_ids_I=[],
models_I={},
time_points_I=[],
sample_name_abbreviations_I=[],
measured_concentration_coverage_criteria_I=0.5,
measured_dG_f_coverage_criteria_I=0.99,
filename='tacomparison.csv'):
'''export concentration and dG_r data for visualization'''
# get the model ids:
data_O = [];
if model_ids_I:
model_ids = model_ids_I;
else:
model_ids = [];
model_ids = self.get_modelID_experimentID_dataStage03QuantificationSimulation(experiment_id_I);
for model_id in model_ids:
print('exporting thermodynamic analysis for model_id ' + model_id);
# get the cobra model
if models_I:
cobra_model = models_I[model_id];
else:
cobra_model_sbml = None;
cobra_model_sbml = self.get_row_modelID_dataStage02PhysiologyModels(model_id);
# write the model to a temporary file
with open('data/cobra_model_tmp.xml','w') as file:
file.write(cobra_model_sbml['model_file']);
# Read in the sbml file and define the model conditions
cobra_model = create_cobra_model_from_sbml_file('data/cobra_model_tmp.xml', print_time=True);
# get the time-points
if time_points_I:
time_points = time_points_I;
else:
time_points = [];
time_points = self.get_timePoints_experimentIDAndModelID_dataStage03QuantificationSimulation(experiment_id_I,model_id);
for tp in time_points:
print('exporting thermodynamic analysis for time_point ' + tp);
# get sample_name_abbreviations
if sample_name_abbreviations_I:
sample_name_abbreviations = sample_name_abbreviations_I;
else:
sample_name_abbreviations = [];
sample_name_abbreviations = self.get_sampleNameAbbreviations_experimentIDAndModelIDAndTimePoint_dataStage03QuantificationSimulation(experiment_id_I,model_id,tp);
sample_name_abbreviations = [sna for sna in sample_name_abbreviations if sna !=sample_name_abbreviation_base]
# get information about the sample to be compared
concentrations_base = {};
concentrations_base = self.get_rowsEscher_experimentIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationMetabolomicsData(experiment_id_I,tp,sample_name_abbreviation_base);
# get tcc
tcc_base = [];
tcc_base = self.get_rows_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationTCC(experiment_id_I,model_id,tp,sample_name_abbreviation_base,measured_concentration_coverage_criteria_I,measured_dG_f_coverage_criteria_I);
for sna in sample_name_abbreviations:
print('exporting thermodynamic analysis for sample_name_abbreviation ' + sample_name_abbreviation_base+'_vs_'+sna);
for tcc_b in tcc_base:
# get tcc
tcc = {};
tcc = self.get_row_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationTCC(experiment_id_I,model_id,tp,sna,tcc_b['rxn_id'],tcc_b['dG_r_lb'],tcc_b['dG_r_ub'],measured_concentration_coverage_criteria_I,measured_dG_f_coverage_criteria_I);
# record data
if tcc:
# test for statistical and biological significance
significant_stat=self.check_significanceStatistical(tcc_b['dG_r_lb'],tcc_b['dG_r_ub'],tcc['dG_r_lb'],tcc['dG_r_ub']);
significant_bio=self.check_significanceBiological(tcc_b['dG_r_lb'],tcc_b['dG_r_ub'],tcc['dG_r_lb'],tcc['dG_r_ub']);
data_O.append({'experiment_id':experiment_id_I,
'model_id':model_id,
'sample_name_abbreviation':sna,
'time_point':tp,
'rxn_id':tcc['rxn_id'],
'dG_r_units':tcc['dG_r_units'],
'dG_r_lb_base':tcc_b['dG_r_lb'],
'dG_r_lb':tcc['dG_r_lb'],
'dG_r_ub_base':tcc_b['dG_r_ub'],
'dG_r_ub':tcc['dG_r_ub'],
'displacement_lb_base':tcc_b['displacement_lb'],
'displacement_lb':tcc['displacement_lb'],
'displacement_ub_base':tcc_b['displacement_ub'],
'displacement_ub':tcc['displacement_ub'],
'feasible_base':tcc_b['feasible'],
'feasible':tcc['feasible'],
'significant_stat':significant_stat,
'significant_bio':significant_bio});
# write data to csv
headers = ['experiment_id',
'model_id',
'sample_name_abbreviation',
'time_point',
'rxn_id',
'dG_r_units',
'dG_r_lb_base',
'dG_r_ub_base',
'dG_r_lb',
'dG_r_ub',
'displacement_lb_base',
'displacement_lb',
'displacement_ub_base',
'displacement_ub',
'feasible_base',
'feasible',
'significant_stat',
'significant_bio']
io = base_exportData(data_O);
filename_str = filename.split('.')[0] + experiment_id_I + '.' + filename.split('.')[1];
io.write_dict2csv(filename_str,headers);
